# import libraries
library("tidyverse")
library("DESeq2")
library("knitr")
library('kableExtra')
library("RColorBrewer")
library("pheatmap")
#library("scatterplot3d")
#library("gplots")
library("dplyr")
#library("rBLAST")
#library("Biostrings")
library("BiocParallel")
library("broman")
library("gridExtra")
library("ggpubr")
library("FactoMineR")
library("factoextra")
register(MulticoreParam(12))
"
Define threshold for signature miRNA, including effect size, 
significance therhold, and expression size
"
## [1] "\nDefine threshold for signature miRNA, including effect size, \nsignificance therhold, and expression size\n"
lfc.Threshold <- 0.5849625
rpm.Threshold <- 100
p.Threshold   <- 0.05

Import MirGeneDB metadata

MirGeneDB_info <- read_delim('/Users/eirikhoy/Dropbox/projects/comet_analysis/data/hsa_MirGeneDB_to_miRBase.csv', delim = ';')
## Parsed with column specification:
## cols(
##   MirGeneDB_ID = col_character(),
##   MiRBase_ID = col_character(),
##   Family = col_character(),
##   Seed = col_character(),
##   `5p accession` = col_character(),
##   `3p accession` = col_character(),
##   Chromosome = col_character(),
##   Start = col_double(),
##   End = col_double(),
##   Strand = col_character(),
##   `Node of origin (locus)` = col_character(),
##   `Node of origin (family)` = col_character(),
##   `3' NTU` = col_character(),
##   ` UG ` = col_double(),
##   UGUG = col_double(),
##   CNNC = col_double()
## )
MirGeneDB_info <- MirGeneDB_info %>% filter(!grepl("-v[2-9]", MirGeneDB_ID)) # keep only -v1
MirGeneDB_info$MirGeneDB_ID <- str_replace_all(MirGeneDB_info$MirGeneDB_ID, "-v1", "")

Functions

DeseqObject <- function(DESIGN, countdata, coldata, consensus="None", sample_type="None", Ref) {
  "
  Function to create DESeq2 object
  "
  
  dds <- DESeqDataSetFromMatrix(countData = countdata,
                                colData = coldata,
                                design = as.formula(paste("~", DESIGN)))
    # Kick out non-consensus samples
  if (!(consensus == "None")) {
    dds <- dds[, dds$paper %in% consensus]
  }

  # Kick out samples that are not bulk tissue
  if (!(sample_type == "None")) {
    dds <- dds[, dds$sample_type == sample_type]
  }

  dds$tissue.type <- relevel(dds$tissue.type, ref=ref)
  dds$tissue.type <- droplevels(dds$tissue.type)
  
  dds <- DESeq(dds,
               parallel=TRUE,
               BPPARAM=MulticoreParam(3)
               )

  return(dds)
}
DeseqResult <- function(dds, column, coef, tissue_type_A, tissue_type_B, 
                        lfc.Threshold, rpm.Threshold,
                        norm_adj_up       = "None",
                        norm_adj_down     = "None",
                        pCRC_adj_up   = "None",
                        pCRC_adj_down = "None"){
  "
  Function to return results from DESeq2 for different conditions, 
  including control for normal adjacent tissue, if available
  "
    p.threshold   <- p.Threshold
    lfc.threshold <- lfc.Threshold
    rpm.threshold <- rpm.Threshold
    samples_tissue_type_A <- colData(dds)[, column] == tissue_type_A
    samples_tissue_type_B <- colData(dds)[, column] == tissue_type_B
  res <- results(dds, name = coef, alpha = p.threshold)
  res <- lfcShrink(dds, coef=coef, res=res)
    rpm <- t(t(counts(dds)) / colSums(counts(dds))) * 1000000
    sig <- rownames(res[(abs(res$log2FoldChange) > lfc.threshold) &
                        (res$padj < p.threshold) &
                        !is.na(res$padj), ])
    sig <- sig[ (rowMeans(rpm[sig, samples_tissue_type_A]) > rpm.threshold) |
                (rowMeans(rpm[sig, samples_tissue_type_B]) > rpm.threshold)
                ]
    res_sig    <- res[sig, ]
    up_mirna   <- rownames(res_sig[res_sig$log2FoldChange > lfc.Threshold, ])
    down_mirna <- rownames(res_sig[res_sig$log2FoldChange < -lfc.Threshold, ])
  
    if (!(norm_adj_up == "None")) {
      up_mirna <- setdiff(up_mirna, norm_adj_up)
    }
    if (!(norm_adj_down == "None")) {
      down_mirna <- setdiff(down_mirna, norm_adj_down)
    }
    if (!(pCRC_adj_up == "None")) {
      up_mirna <- setdiff(up_mirna, pCRC_adj_up)
    }
    if (!(pCRC_adj_down == "None")) {
      down_mirna <- setdiff(down_mirna, pCRC_adj_down)
    }
        
    return_list <- list("rpm" = rpm, "res" = res, "sig"=sig, "res_sig"=res_sig, 
                        "down_mirna"=down_mirna, "up_mirna"=up_mirna)
    
    return(return_list)

}
SigList <- function(res, dds, tissue_type_A, tissue_type_B, coef,
                            norm_adj_up, norm_adj_down, 
                            pCRC_adj_up, pCRC_adj_down){
  "
  Function to create annotated lists of signature miRNA
  Return will print upregulated or downregulated miRNA, 
  by printing <signature_list>$up_mirna
  or          <signature_list>$down_mirna
  "
  group_A_rpm <- rowMeans(res$rpm[res$sig, dds$tissue.type == tissue_type_A])
  group_A_rpm_std <- rowSds(res$rpm[res$sig, dds$tissue.type == tissue_type_A])
  group_B_rpm <- rowMeans(res$rpm[res$sig, dds$tissue.type == tissue_type_B])
  group_B_rpm_std <- rowSds(res$rpm[res$sig, dds$tissue.type == tissue_type_B])
  lfc.deseq2  <- res$res[res$sig, ]$log2FoldChange
  lfcSE.deseq2<- res$res[res$sig, ]$lfcSE
  neg.log.10.adj.p <- format(res$res[res$sig, ]$padj, digits=3)
  signature_mirna <- res$sig
  sig_list <- dplyr::tibble(signature_mirna, lfc.deseq2, lfcSE.deseq2,
                            group_A_rpm, #group_A_rpm_std,
                            group_B_rpm, #group_B_rpm_std,
                            neg.log.10.adj.p)
  sig_list$signature_sub <- str_replace_all(signature_mirna, "/.*", "") %>% str_replace_all(., c("_5p" = "", "_3p" = ""))
  sig_list <- left_join(sig_list, MirGeneDB_info, by=c("signature_sub" = "MirGeneDB_ID"))
  
  # create list of upregulated mirna
  up_mirna <- sig_list %>%
    filter(lfc.deseq2 > lfc.Threshold) %>% 
    
    # Annotate which miRNA are cell markers
    mutate(
      cell_marker = ifelse(signature_mirna %in% names(cell_spec_dict_inv), cell_spec_dict_inv[signature_mirna], '')) %>%
    mutate(
      cell_marker = cell_spec(cell_marker, color = ifelse(cell_marker != '', 'white', 'black'),
                              background = ifelse(cell_marker != '', 'blue', 'white'),
                              bold = ifelse(cell_marker != '', F, F)))

    # Annotate which miRNA are in normal_adjacent
    if (norm_adj_up != "None") {
      up_mirna <- up_mirna %>%
        mutate(
          norm_adj = ifelse(signature_mirna %in% norm_adj_up, 'yes', '')) %>%
        mutate(
          norm_adj = cell_spec(norm_adj, color = ifelse(norm_adj == 'yes', 'white', 'black'),
                               background = ifelse(norm_adj == 'yes', 'black', 'white'),
                               bold = ifelse(norm_adj == 'yes', F, F))
        )
    }
    else up_mirna$norm_adj <- "na"
    
  # Annotate which miRNA are in pCRC_adjacent
    if (pCRC_adj_up != "None") {
      up_mirna <- up_mirna %>%
        mutate(
          pCRC_adj = ifelse(signature_mirna %in% pCRC_adj_up, 'yes', '')) %>%
        mutate(
          pCRC_adj = cell_spec(pCRC_adj, color = ifelse(pCRC_adj == 'yes', 'white', 'black'),
                               background = ifelse(pCRC_adj == 'yes', 'black', 'white'),
                               bold = ifelse(pCRC_adj == 'yes', F, F))
        )
    }
    else up_mirna$pCRC_adj <- "na"

    # number of upregulated miRNA
    number_upregulated <- dim(up_mirna)[1]
    
    # select only relevant rows
    up_mirna <- up_mirna %>% select(signature_mirna, lfc.deseq2, lfcSE.deseq2, neg.log.10.adj.p, 
                                    group_A_rpm,  #group_A_rpm_std,
                                    group_B_rpm, #group_B_rpm_std,
                                    MiRBase_ID, Family, Seed, Chromosome,
                                    cell_marker, norm_adj, pCRC_adj)
    
    
  # Create kable list with annotations
    up_mirna <- up_mirna %>%
      arrange(-lfc.deseq2) %>%
      arrange(desc(cell_marker)) %>%
      arrange(pCRC_adj) %>%
      arrange(norm_adj) %>%
      kable(col.names = c("miRNA", "LFC", "lfcSE", "FDR", 
                          paste('RPM', tissue_type_A), #paste('std', tissue_type_A), 
                          paste('RPM', tissue_type_B), #paste('std', tissue_type_B), 
                          "miRBase_ID", "Family", "Seed", "Chr",
                          "Cell-Type Specific", 'Norm Background', 'pCRC Background'),
            escape = F, booktabs = F, caption = paste("Upregulated in ", coef),
            digits = c(0, 2, 2, 3, 0, 0, 2, 3, 0, 0, 0, 0, 0, 0, 0)) %>%
      kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = T, 
                  fixed_thead = list(enabled = T)) %>%
      scroll_box(width = "2000px")
    
 
  # create list of downregulated miRNA
  down_mirna <- sig_list %>%
    filter(lfc.deseq2 < -lfc.Threshold) %>% 
    
    # Annotate which miRNA are cell markers
    mutate(
      cell_marker = ifelse(signature_mirna %in% names(cell_spec_dict_inv), cell_spec_dict_inv[signature_mirna], '')) %>%
    mutate(
      cell_marker = cell_spec(cell_marker, color = ifelse(cell_marker != '', 'white', 'black'),
                              background = ifelse(cell_marker != '', 'blue', 'white'),
                              bold = ifelse(cell_marker != '', F, F)))

    # Annotate which miRNA are in normal_adjacent
    if (norm_adj_down != "None") {
      down_mirna <- down_mirna %>%
        mutate(
          norm_adj = ifelse(signature_mirna %in% norm_adj_down, 'yes', '')) %>%
        mutate(
          norm_adj = cell_spec(norm_adj, color = ifelse(norm_adj == 'yes', 'white', 'black'),
                               background = ifelse(norm_adj == 'yes', 'black', 'white'),
                               bold = ifelse(norm_adj == 'yes', F, F))
        )
    }
    else down_mirna$norm_adj <- "na"

    # Annotate which miRNA are in pCRC_adjacent
    if (pCRC_adj_down != "None") {
      down_mirna <- down_mirna %>%
        mutate(
          pCRC_adj = ifelse(signature_mirna %in% pCRC_adj_down, 'yes', '')) %>%
        mutate(
          pCRC_adj = cell_spec(pCRC_adj, color = ifelse(pCRC_adj == 'yes', 'white', 'black'),
                               background = ifelse(pCRC_adj == 'yes', 'black', 'white'),
                               bold = ifelse(pCRC_adj == 'yes', F, F))
        )
    }
    else down_mirna$pCRC_adj <- "na"
  
    # number of upregulated miRNA
    number_downregulated <- dim(down_mirna)[1]

    down_mirna <- down_mirna %>% select(signature_mirna, lfc.deseq2, lfcSE.deseq2, neg.log.10.adj.p,
                                        group_A_rpm,  #group_A_rpm_std,
                                        group_B_rpm, #group_B_rpm_std,
                                        MiRBase_ID, Family, Seed, Chromosome,
                                        cell_marker, norm_adj, pCRC_adj)    
    
  # Create kable list with annotations    
    down_mirna <- down_mirna %>%
      arrange(lfc.deseq2) %>%
      arrange(desc(cell_marker)) %>%
      arrange(pCRC_adj) %>%
      arrange(norm_adj) %>%
      kable(col.names = c("miRNA", "LFC", "lfcSE", "FDR",
                          paste('RPM', tissue_type_A), #paste('std', tissue_type_A), 
                          paste('RPM', tissue_type_B), #paste('std', tissue_type_B), 
                          "miRBase_ID", "Family", "Seed", "Chr",
                           "Cell-Type Specific", 'Norm Background', 'pCRC Background'),
            escape = F, booktabs = F, caption = paste("Downregulated in ", coef),
            digits = c(0, 2, 2, 3, 0, 0, 2, 3, 0, 0, 0, 0, 0, 0, 0)) %>%
      kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = T, 
                  fixed_thead = list(enabled = T)) %>%
       scroll_box(width = "2000px")

  # Function return is to print kable, either upregulated or downregulated miRNA
  return_list = list("up_mirna" = up_mirna, "down_mirna" = down_mirna, 
                     "number_upregulated" = number_upregulated, 
                     "number_downregulated" = number_downregulated)
  return(return_list)
}    
# Read the sample information into a data frame
sampleinfo <- read_delim("/Users/eirikhoy/Dropbox/projects/comet_analysis/data/sample_info_v9.csv", delim=';')
## Parsed with column specification:
## cols(
##   filename = col_character(),
##   paper = col_character(),
##   sample_name = col_character(),
##   type.tissue = col_character(),
##   type = col_character(),
##   tissue = col_character(),
##   paper_sample_name = col_character(),
##   `3p-adapter` = col_character(),
##   qc_report = col_character(),
##   malignant = col_character(),
##   new_old = col_character()
## )
sampleinfo <- sampleinfo %>%
  filter(qc_report == 'keep')
sampleinfo$filename <- str_remove(sampleinfo$filename, '.fasta.fas.gz.bam')
sampleinfo$filename <- str_remove(sampleinfo$filename, '.fasta.bam')
sampleinfo$filename <- str_replace_all(sampleinfo$filename, pattern = '\\.', replacement = '_')
sampleinfo$filename <- str_replace_all(sampleinfo$filename, pattern = '-', replacement = '_')
#sampleinfo$filename <- str_replace_all(sampleinfo$filename, pattern = '__', replacement = '_')

# Read the data into R
#seqdata <- read_delim("/Users/eirikhoy/Dropbox/projects/comet_analysis/data/count_matrix_08.12.20.csv", delim = ';')
seqdata_1 <- read_delim("/Users/eirikhoy/Dropbox/projects/mirge3/output_dir/miRge.2021-01-19_10-03-25/miR.Counts.csv", delim = ',')
## Parsed with column specification:
## cols(
##   .default = col_double(),
##   miRNA = col_character()
## )
## See spec(...) for full column specifications.
#seqdata_2 <- read_delim("/Users/eirikhoy/Dropbox/projects/mirge3/output_dir/miRge.2021-01-19_12-04-26/miR.Counts.csv", delim = ',')
seqdata_3 <- read_delim("/Users/eirikhoy/Dropbox/projects/mirge3/output_dir/miRge.2021-01-19_15-00-38/miR.Counts.csv", delim = ',')
## Parsed with column specification:
## cols(
##   miRNA = col_character(),
##   SRR1273998 = col_double(),
##   SRR1273999 = col_double(),
##   SRR1274000 = col_double(),
##   SRR1274001 = col_double()
## )
seqdata_4 <- read_delim("/Users/eirikhoy/Dropbox/projects/mirge3/output_dir/miRge.2021-01-19_15-33-44/miR.Counts.csv", delim = ',')
## Parsed with column specification:
## cols(
##   .default = col_double(),
##   miRNA = col_character()
## )
## See spec(...) for full column specifications.
seqdata_5 <- read_delim("/Users/eirikhoy/Dropbox/projects/mirge3/output_dir/miRge.2021-01-19_17-33-30/miR.Counts.csv", delim = ',')
## Parsed with column specification:
## cols(
##   .default = col_double(),
##   miRNA = col_character()
## )
## See spec(...) for full column specifications.
seqdata_6 <- read_delim("/Users/eirikhoy/Dropbox/projects/mirge3/output_dir/miRge.2021-02-04_08-16-37/miR.Counts.csv", delim = ',')
## Parsed with column specification:
## cols(
##   .default = col_double(),
##   miRNA = col_character()
## )
## See spec(...) for full column specifications.
seqdata_7 <- read_delim("/Users/eirikhoy/Dropbox/projects/mirge3/output_dir/miRge.2021-02-04_09-09-56/miR.Counts.csv", delim = ',')
## Parsed with column specification:
## cols(
##   .default = col_double(),
##   miRNA = col_character()
## )
## See spec(...) for full column specifications.
seqdata_8 <- read_delim("/Users/eirikhoy/Dropbox/projects/mirge3/output_dir/miRge.2021-02-04_12-11-38/miR.Counts.csv", delim = ',')
## Parsed with column specification:
## cols(
##   .default = col_double(),
##   miRNA = col_character()
## )
## See spec(...) for full column specifications.
seqdata <- inner_join(inner_join(inner_join(inner_join(inner_join(inner_join(seqdata_1, seqdata_3), seqdata_4), seqdata_5), seqdata_6), seqdata_7), seqdata_8)
## Joining, by = "miRNA"
## Joining, by = "miRNA"
## Joining, by = "miRNA"
## Joining, by = "miRNA"
## Joining, by = "miRNA"
## Joining, by = "miRNA"
colnames(seqdata) <- str_replace_all(colnames(seqdata), pattern='-', replacement = '_')
#colnames(seqdata) <- str_replace_all(colnames(seqdata), pattern='__', replacement = '_')

seqdata <- seqdata[- grep("\\*", seqdata$miRNA), ]
seqdata <- seqdata %>% filter(str_detect(miRNA , "chr", negate = TRUE))
#colnames(seqdata)[2:length(colnames(seqdata))] <- sampleinfo$sample

# Format the data
countdata <- seqdata %>%
  column_to_rownames("miRNA") %>%
  #rename_all(str_remove, ".bam") %>%
  select(sampleinfo$filename) %>%
  as.matrix()

# List consensus samples
consensus <- c("neerincx", "fromm", "schee", "selitsky")

# create the design formula
sampleinfo$tissue.type <- as.factor(paste(sampleinfo$type, sampleinfo$tissue, sep="."))
sampleinfo$type <- as.factor(sampleinfo$type)
design <- as.formula(~ tissue.type)

Differential Expression

# Make a named list of signature miRNA
dict_sig_mirna <- c()
res_dict <- list()
ref <- 'normal.colorect'
dds <- DeseqObject(design, countdata, sampleinfo, "None", "None", ref)
# #datasets in total
dim(dds[, colData(dds)$type.tissue == 'pCRC'])
## [1] 389 120
dim(dds[, colData(dds)$type.tissue == 'mLi'])
## [1] 389  35
dim(dds[, colData(dds)$type.tissue == 'mLu'])
## [1] 389  28
dim(dds[, colData(dds)$type.tissue == 'nCR'])
## [1] 389  25
dim(dds[, colData(dds)$type.tissue == 'nLi'])
## [1] 389  20
dim(dds[, colData(dds)$type.tissue == 'nLu'])
## [1] 389  10
dim(dds[, colData(dds)$type.tissue == 'PM'])
## [1] 389  30
# #datasets for Fromm
dim(dds[, colData(dds)$type.tissue == 'pCRC' & colData(dds)$paper == 'fromm'])
## [1] 389   3
dim(dds[, colData(dds)$type.tissue == 'mLi' & colData(dds)$paper == 'fromm'])
## [1] 389  19
dim(dds[, colData(dds)$type.tissue == 'mLu' & colData(dds)$paper == 'fromm'])
## [1] 389  24
dim(dds[, colData(dds)$type.tissue == 'nCR' & colData(dds)$paper == 'fromm'])
## [1] 389   3
dim(dds[, colData(dds)$type.tissue == 'nLi' & colData(dds)$paper == 'fromm'])
## [1] 389   8
dim(dds[, colData(dds)$type.tissue == 'nLu' & colData(dds)$paper == 'fromm'])
## [1] 389   7
dim(dds[, colData(dds)$type.tissue == 'PM' & colData(dds)$paper == 'fromm'])
## [1] 389  18
# #datasets for Schee
dim(dds[, colData(dds)$type.tissue == 'pCRC' & colData(dds)$paper == 'schee'])
## [1] 389  83
dim(dds[, colData(dds)$type.tissue == 'mLi' & colData(dds)$paper == 'schee'])
## [1] 389   0
dim(dds[, colData(dds)$type.tissue == 'mLu' & colData(dds)$paper == 'schee'])
## [1] 389   0
dim(dds[, colData(dds)$type.tissue == 'nCR' & colData(dds)$paper == 'schee'])
## [1] 389   0
dim(dds[, colData(dds)$type.tissue == 'nLi' & colData(dds)$paper == 'schee'])
## [1] 389   0
dim(dds[, colData(dds)$type.tissue == 'nLu' & colData(dds)$paper == 'schee'])
## [1] 389   0
dim(dds[, colData(dds)$type.tissue == 'PM' & colData(dds)$paper == 'schee'])
## [1] 389   0
# #datasets for Schee
dim(dds[, colData(dds)$type.tissue == 'pCRC' & colData(dds)$paper == 'neerincx'])
## [1] 389  34
dim(dds[, colData(dds)$type.tissue == 'mLi' & colData(dds)$paper == 'neerincx'])
## [1] 389  16
dim(dds[, colData(dds)$type.tissue == 'mLu' & colData(dds)$paper == 'neerincx'])
## [1] 389   4
dim(dds[, colData(dds)$type.tissue == 'nCR' & colData(dds)$paper == 'neerincx'])
## [1] 389  22
dim(dds[, colData(dds)$type.tissue == 'nLi' & colData(dds)$paper == 'neerincx'])
## [1] 389   9
dim(dds[, colData(dds)$type.tissue == 'nLu' & colData(dds)$paper == 'neerincx'])
## [1] 389   3
dim(dds[, colData(dds)$type.tissue == 'PM' & colData(dds)$paper == 'neerincx'])
## [1] 389  12
# #datasets for Schee
dim(dds[, colData(dds)$type.tissue == 'pCRC' & colData(dds)$paper == 'selitsky'])
## [1] 389   0
dim(dds[, colData(dds)$type.tissue == 'mLi' & colData(dds)$paper == 'selitsky'])
## [1] 389   0
dim(dds[, colData(dds)$type.tissue == 'mLu' & colData(dds)$paper == 'selitsky'])
## [1] 389   0
dim(dds[, colData(dds)$type.tissue == 'nCR' & colData(dds)$paper == 'selitsky'])
## [1] 389   0
dim(dds[, colData(dds)$type.tissue == 'nLi' & colData(dds)$paper == 'selitsky'])
## [1] 389   3
dim(dds[, colData(dds)$type.tissue == 'nLu' & colData(dds)$paper == 'selitsky'])
## [1] 389   0
dim(dds[, colData(dds)$type.tissue == 'PM' & colData(dds)$paper == 'selitsky'])
## [1] 389   0
## Plot dispersion estimates
plotDispEsts(dds)

McCall, Matthew N; Kim, Min-Sik; Adil, Mohammed; Patil, Arun H; Lu, Yin; Mitchell, Christopher J; Leal-Rojas, Pamela; Xu, Jinchong; Kumar, Manoj; Dawson, Valina L; Dawson, Ted M; Baras, Alexander S; Rosenberg, Avi Z; Arking, Dan E; Burns, Kathleen H; Pandey, Akhilesh; Halushka, Marc K Toward the human cellular microRNAome Genome Res. October 2017

cell_spec_dict <- list(
  "CD14+ Monocyte"   = c("Hsa-Mir-15-P1a_5p","Hsa-Mir-15-P1b_5p", "Hsa-Mir-17-P1a_5p/P1b_5p"),
  
  "Dendritic Cell"   = c("Hsa-Mir-146-P2_5p", "Hsa-Mir-342_3p", "Hsa-Mir-142_3p",
                         "Hsa-Mir-223_3p"),
  
  "Endothelial Cell" = c("Hsa-Mir-126_5p"),
  
  "Epithelial Cell"  = c("Hsa-Mir-8-P2a_3p", "Hsa-Mir-8-P2b_3p", "Hsa0Mir-205-P1_5p",
                        "Hsa-Mir-192-P1_5p/P2_5p", "Hsa-Mir-375_3p"),
  
  "Islet Cell"       = c("Hsa-Mir-375_3p", "Hsa-Mir-154-P7_5p", "Hsa-Mir-7-P1_5p/P2_5p/P3_5p"),
  
  "Lymphocyte"       = c("Hsa-Mir-146-P2_5p", "Hsa-Mir-342_3p", "Hsa-Mir-150_5p",
                        "Hsa-Mir-155_5p"),
  
  "Macrophage"       = c("Hsa-Mir-342_3p", "Hsa-Mir-142_3p", "Hsa-Mir-223_3p", 
                         "Hsa-Mir-155_5p", "Hsa-Mir-24-P1_3p/P2_3p",
                         "Hsa-Mir-185_5p"),
  
  "Melanocyte"       = c("Hsa-Mir-185_5p", "Hsa-Mir-204-P2_5p"),
  
  "Mesenchymal"      = c("Hsa-Mir-185_5p", "Hsa-Mir-143_3p", "Hsa-Mir-145_5p"),
  
  "Neural"           = c("Hsa-Mir-375_3p", "Hsa-Mir-154-P7_5p", "Hsa-Mir-7-P1_5p/P2_5p/P3_5p",
                         "Hsa-Mir-128-P1_3p/P2_3p", "Hsa-Mir-129-P1_5p/P2_5p",
                         "Hsa-Mir-9-P1_5p/P2_5p/P3_5p","Hsa-Mir-430-P2_3p",
                         "Hsa-Mir-430-P4_3p"),
  
  "Platelet"         = c("Hsa-Mir-126_5p", "Hsa-Mir-486_5p"),
  
  "Red Blood Cell"   = c("Hsa-Mir-486_5p", "Hsa-Mir-451_5p", "Hsa-Mir-144_5p"),
  
  "Retinal Epithelial Cell" = c("Hsa-Mir-204-P1_5p", "Hsa-Mir-204-P2_5p", "Hsa-Mir-335_5p"),
  
  "Skeletal Myocyte" = c("Hsa-Mir-1-P1_3p/P2_3p", "Hsa-Mir-133-P1_3p/P2_3p/P3_3p"),
  
  "Stem Cell"        = c("Hsa-Mir-430-P2_3p", "Hsa-Mir-430-P4_3p", "Hsa-Mir-133-P1_3p/P2_3p/P3_3p"),
  
  "Hepatocyte"        = c("Hsa-Mir-122_5p")
  )

cell_spec_dict_inv <- topGO::inverseList(cell_spec_dict)
##

Pritchard, C C; Kroh, E; Wood, B; Arroyo, J D; Dougherty, K J; Miyaji, M M; Tait, J F; Tewari, M Blood Cell Origin of Circulating MicroRNAs: A Cautionary Note for Cancer Biomarker Studies Cancer Prevention Research 2012

blood.cell.mirna <- c("Hsa-Mir-223_3p",
                      "Hsa-Mir-15-P2a_5p",
                      "Hsa-Mir-15-P2b_5p",
                      "Hsa-Mir-126-v1_3p",
                      "Hsa-Mir-142-v1_3p",
                      "Hsa-Mir-21_5p",
                      "Hsa-Mir-24-P1_3p",
                      "Hsa-Mir-24-P2_3p",
                      "Hsa-Mir-19-P2a_3p",
                      "Hsa-Mir-19-P2b_3p",
                      "Hsa-Mir-103-P1_3p",
                      "Hsa-Mir-103-P2_3p",
                      "Hsa-Let-7-P1a_5p",
                      "Hsa-Let-7-P2a1_5p",
                      "Hsa-Let-7-P2a2_5p",
                      "Hsa-Mir-451_5p",
                      "Hsa-Mir-92-P1a_3p",
                      "Hsa-Mir-92-P1b_3p",
                      "Hsa-Mir-17-P1b_5p",
                      "Hsa-Mir-19-P1_3p",
                      "Hsa-Mir-30-P2c_5p",
                      "Hsa-Mir-17-P1a",
                      "Hsa-Mir-15-P1b_5p",
                      "Hsa-Mir-103-P3_3p",
                      "Hsa-Let-7-P2a3_5p",
                      "Hsa-Let-7-P2b1_5p",
                      "Hsa-Mir-221-P1_3p",
                      "Hsa-Mir-221-P2_3p",
                      "Hsa-Mir-17-P1c_5p",
                      "Hsa-Mir-30-P2a_5p",
                      "Hsa-Mir-30-P2b_5p",
                      "Hsa-Mir-28-P2_5p",
                      "Hsa-Mir-30-P1b_5p",
                      "Hsa-Mir-30-P1c_5p",
                      "Hsa-Mir-486_5p",
                      "Hsa-Mir-92-P2c_3p",
                      "Hsa-Mir-181-P1a_5p",
                      "Hsa-Mir-181-P1b_5p",
                      "Hsa-Mir-146-P1_5p",
                      "Hsa-Let-7-P2c1_5p",
                      "Hsa-Mir-197_3p",
                      "Hsa-Mir-17-P3c_5p",
                      "Hsa-Mir-17-P3c_3p",
                      "Hsa-Mir-148-P3_3p",
                      "Hsa-Mir-766_3p",
                      "Hsa-Mir-17-P3b_5p",
                      "Hsa-Mir-328_3p",
                      "Hsa-Mir-574_3p",
                      "Hsa-Mir-155_5p",
                      "Hsa-Mir-425_5p",
                      "Hsa-Mir-148-P1_3p",
                      "Hsa-Mir-29-P1a_3p",
                      "Hsa-Mir-8-P2b_3p",
                      "Hsa-Mir-92-P1c_3p",
                      "Hsa-Mir-192-P2_5p",
                      "Hsa-Mir-362-P2-v1_3p",
                      "Hsa-Mir-362-P5_5p"
                      )

nCR vs nLi

column='tissue.type'
tissue_type_A <- 'normal.liver'
tissue_type_B <- 'normal.colorect'
norm_adj_up   = "None"
norm_adj_down = "None"
pCRC_adj_up   = "None"
pCRC_adj_down = "None"

coef <- paste(column, tissue_type_A, 'vs', tissue_type_B, sep='_')
res <- DeseqResult(dds, column, coef, tissue_type_A, tissue_type_B,
                   lfc.Threshold, rpm.Threshold,
                   norm_adj_up,
                   norm_adj_down)
dict_sig_mirna[paste(coef, "up",   sep='_')] <- list(res$up_mirna)
dict_sig_mirna[paste(coef, "down", sep='_')] <- list(res$down_mirna)
res_res <- res$res
res_dict[coef] <- res_res
plotMA(res$res, alpha=0.05)

# Plot volcano plot
VolcanoPlot(res$res, coef, res$sig,
            res$up_mirna, res$down_mirna,
            norm_adj_up, norm_adj_down,
            pCRC_adj_up, pCRC_adj_down)

ExpressionPlot(res$res, res$rpm, coef, res$sig,
               tissue_type_A, tissue_type_B,
               res$up_mirna, res$down_mirna,
               norm_adj_up, norm_adj_down,
               pCRC_adj_up, pCRC_adj_down)

signature_mirnas <- SigList(res, dds, tissue_type_A, tissue_type_B, coef,
                            norm_adj_up, norm_adj_down, 
                            pCRC_adj_up, pCRC_adj_down)
# Print list upregulated miRNA
signature_mirnas$up_mirna
Upregulated in tissue.type_normal.liver_vs_normal.colorect
miRNA LFC lfcSE FDR RPM normal.liver RPM normal.colorect miRBase_ID Family Seed Chr Cell-Type Specific Norm Background pCRC Background
Hsa-Mir-204-P1_5p 1.56 0.51 4.46e-03 190 43 hsa-mir-204 MIR-204 UCCCUUU chr9 Retinal Epithelial Cell na na
Hsa-Mir-335_5p 1.18 0.17 2.56e-11 205 79 hsa-mir-335 MIR-335 CAAGAGC chr7 Retinal Epithelial Cell na na
Hsa-Mir-144_5p 1.53 0.33 5.21e-05 206 65 hsa-mir-144 MIR-144 GAUAUCA chr17 Red Blood Cell na na
Hsa-Mir-128-P1_3p/P2_3p 0.65 0.16 2.62e-04 194 110 hsa-mir-128-1 MIR-128 CACAGUG chr2 Neural na na
Hsa-Mir-122_5p 10.58 0.78 7.97e-28 148842 26 hsa-mir-122 MIR-122 GGAGUGU chr18 Hepatocyte na na
Hsa-Mir-486_5p 1.34 0.36 2.99e-03 10617 3961 hsa-mir-486-1 MIR-486 CCUGUAC chr8 c(“Platelet”, “Red Blood Cell”) na na
Hsa-Mir-126_5p 1.25 0.21 1.87e-08 11148 4230 hsa-mir-126 MIR-126 AUUAUUA chr9 c(“Endothelial Cell”, “Platelet”) na na
Hsa-Mir-885_5p 8.77 0.70 8.60e-24 510 0 hsa-mir-885 MIR-885 CCAUUAC chr3 na na
Hsa-Mir-483_5p 4.03 0.71 1.04e-10 115 1 hsa-mir-483 MIR-483 AGACGGG chr11 na na
Hsa-Let-7-P1c_5p 3.18 0.28 1.60e-27 4945 466 hsa-let-7c LET-7 GAGGUAG chr21 na na
Hsa-Mir-10-P2c_5p 3.08 0.33 6.34e-19 1202 114 hsa-mir-99a MIR-10 ACCCGUA chr21 na na
Hsa-Mir-455_5p 2.48 0.18 9.25e-41 279 44 hsa-mir-455 MIR-455 AUGUGCC chr9 na na
Hsa-Mir-193-P2a_3p/P2b_3p 2.37 0.23 5.29e-22 219 37 hsa-mir-365b MIR-193 AAUGCCC chr17 na na
Hsa-Mir-193-P1b_3p 2.34 0.23 2.49e-22 514 90 hsa-mir-193b MIR-193 ACUGGCC chr16 na na
Hsa-Mir-15-P1d_5p 2.26 0.30 5.34e-14 247 40 hsa-mir-424 MIR-15 AGCAGCA chrX na na
Hsa-Mir-193-P1a_5p 2.14 0.28 3.66e-12 298 57 hsa-mir-193a MIR-193 GGGUCUU chr17 na na
Hsa-Mir-10-P3c_5p 2.12 0.30 6.48e-11 1620 326 hsa-mir-125b-2 MIR-10 CCCUGAG chr21 na na
Hsa-Mir-139_5p 2.00 0.26 1.06e-11 174 41 hsa-mir-139 MIR-139 CUACAGU chr11 na na
Hsa-Mir-148-P1_3p 2.00 0.24 8.34e-15 105086 21730 hsa-mir-148a MIR-148 CAGUGCA chr7 na na
Hsa-Mir-10-P3a_5p 1.93 0.31 1.24e-08 611 137 hsa-mir-125b-1 MIR-10 CCCUGAG chr11 na na
Hsa-Mir-423_5p 1.62 0.24 1.23e-09 1198 345 hsa-mir-423 MIR-423 GAGGGGC chr17 na na
Hsa-Mir-92-P1a_3p/P1b_3p 1.60 0.22 1.98e-12 51173 13849 hsa-mir-92a-1 MIR-92 AUUGCAC chr13 na na
Hsa-Mir-101-P1_3p/P2_3p 1.58 0.18 3.44e-17 14649 4197 hsa-mir-101-1 MIR-101 UACAGUA chr1 na na
Hsa-Mir-22-P1a_3p 1.58 0.17 1.76e-18 78932 23453 hsa-mir-22 MIR-22 AGCUGCC chr17 na na
Hsa-Mir-340_5p 1.42 0.16 3.45e-18 1030 337 hsa-mir-340 MIR-340 UAUAAAG chr5 na na
Hsa-Mir-30-P1a_5p 1.32 0.23 1.07e-07 15175 5150 hsa-mir-30a MIR-30 GUAAACA chr6 na na
Hsa-Mir-744_5p 1.31 0.21 1.28e-08 165 59 hsa-mir-744 MIR-744 GCGGGGC chr17 na na
Hsa-Mir-574_3p 1.21 0.19 1.27e-08 745 305 hsa-mir-574 MIR-574 ACGCUCA chr4 na na
Hsa-Mir-130-P1a_3p 1.09 0.20 4.15e-07 907 379 hsa-mir-130a MIR-130 AGUGCAA chr11 na na
Hsa-Mir-10-P2a_5p 1.07 0.32 3.39e-03 2634 986 hsa-mir-100 MIR-10 ACCCGUA chr11 na na
Hsa-Mir-30-P2a_5p/P2b_5p/P2c_5p 0.99 0.15 4.69e-10 5891 2677 hsa-mir-30c-2 MIR-30 GUAAACA chr6 na na
Hsa-Mir-154-P23_3p 0.89 0.20 5.21e-05 222 108 hsa-mir-654 MIR-154 AUGUCUG chr14 na na
Hsa-Mir-197_3p 0.79 0.19 2.06e-04 344 180 hsa-mir-197 MIR-197 UCACCAC chr1 na na
Hsa-Mir-214_3p 0.73 0.23 5.91e-03 137 74 hsa-mir-214 MIR-214 CAGCAGG chr1 na na
Hsa-Mir-30-P1b_5p 0.71 0.15 7.77e-06 11240 6046 hsa-mir-30e MIR-30 GUAAACA chr1 na na
Hsa-Mir-30-P1c_5p 0.70 0.16 7.79e-05 13680 7243 hsa-mir-30d MIR-30 GUAAACA chr8 na na 
# Number of upregulated miRNA
signature_mirnas$number_upregulated
## [1] 36
# Print list downregulated miRNA
signature_mirnas$down_mirna
Downregulated in tissue.type_normal.liver_vs_normal.colorect
miRNA LFC lfcSE FDR RPM normal.liver RPM normal.colorect miRBase_ID Family Seed Chr Cell-Type Specific Norm Background pCRC Background
Hsa-Mir-145_5p -2.51 0.32 5.55e-15 484 2832 hsa-mir-145 MIR-145 UCCAGUU chr5 Mesenchymal na na
Hsa-Mir-143_3p -2.35 0.27 1.80e-17 37572 164812 hsa-mir-143 MIR-143 GAGAUGA chr5 Mesenchymal na na
Hsa-Mir-24-P1_3p/P2_3p -0.63 0.20 5.98e-03 319 413 hsa-mir-24-2 MIR-24 GGCUCAG chr19 Macrophage na na
Hsa-Mir-8-P2b_3p -6.38 0.23 7.72e-155 31 2444 hsa-mir-200c MIR-8 AAUACUG chr12 Epithelial Cell na na
Hsa-Mir-8-P2a_3p -4.47 0.24 4.08e-76 369 7741 hsa-mir-200b MIR-8 AAUACUG chr1 Epithelial Cell na na
Hsa-Mir-192-P1_5p/P2_5p -0.72 0.32 1.03e-02 29546 46010 hsa-mir-192 MIR-192 UGACCUA chr11 Epithelial Cell na na
Hsa-Mir-15-P1b_5p -0.68 0.16 1.18e-04 114 168 hsa-mir-15b MIR-15 AGCAGCA chr3 CD14+ Monocyte na na
Hsa-Mir-133-P1_3p/P2_3p/P3_3p -4.10 0.38 9.37e-29 22 460 hsa-mir-133a-2 MIR-133 UUGGUCC chr20 c(“Skeletal Myocyte”, “Stem Cell”) na na
Hsa-Mir-155_5p -1.51 0.26 8.41e-08 113 295 hsa-mir-155 MIR-155 UAAUGCU chr21 c(“Lymphocyte”, “Macrophage”) na na
Hsa-Mir-375_3p -2.65 0.37 1.69e-12 2553 17215 hsa-mir-375 MIR-375 UUGUUCG chr2 c(“Epithelial Cell”, “Islet Cell”, “Neural”) na na
Hsa-Mir-196-P1_5p/P2_5p -6.76 0.34 3.97e-79 2 250 hsa-mir-196a-1 MIR-196 AGGUAGU chr17 na na
Hsa-Mir-147_3p -6.57 0.38 2.57e-64 1 110 hsa-mir-147b MIR-147 UGUGCGG chr15 na na
Hsa-Mir-577_5p -6.02 0.34 1.65e-65 2 138 hsa-mir-577 MIR-577 UAGAUAA chr4 na na
Hsa-Mir-8-P1b_3p -6.00 0.30 3.97e-79 154 9180 hsa-mir-141 MIR-8 AACACUG chr12 na na
Hsa-Mir-196-P3_5p -5.53 0.37 2.24e-42 9 400 hsa-mir-196b MIR-196 AGGUAGU chr7 na na
Hsa-Mir-10-P1b_5p -4.80 0.36 6.31e-38 2704 75067 hsa-mir-10b MIR-10 ACCCUGU chr2 na na
Hsa-Mir-8-P3a_3p -4.32 0.24 2.00e-68 68 1282 hsa-mir-429 MIR-8 AAUACUG chr1 na na
Hsa-Mir-8-P1a_3p -4.10 0.24 4.38e-64 108 1754 hsa-mir-200a MIR-8 AACACUG chr1 na na
Hsa-Mir-190-P1_5p -3.71 0.26 4.48e-45 23 294 hsa-mir-190a MIR-190 GAUAUGU chr15 na na
Hsa-Mir-96-P3_5p -3.57 0.32 1.72e-22 27 224 hsa-mir-183 MIR-96 AUGGCAC chr7 na na
Hsa-Mir-203_3p -2.68 0.28 6.56e-18 171 922 hsa-mir-203a MIR-203 UGAAAUG chr14 na na
Hsa-Mir-96-P2_5p -2.65 0.24 2.99e-23 370 1867 hsa-mir-182 MIR-96 UUGGCAA chr7 na na
Hsa-Mir-221-P1_3p -2.30 0.19 5.81e-32 217 964 hsa-mir-221 MIR-221 GCUACAU chrX na na
Hsa-Mir-338-P1_3p -1.89 0.31 1.21e-08 48 166 hsa-mir-338 MIR-338 CCAGCAU chr17 na na
Hsa-Mir-221-P2_3p -1.87 0.20 9.25e-18 173 574 hsa-mir-222 MIR-221 GCUACAU chrX na na
Hsa-Mir-10-P1a_5p -1.77 0.30 1.66e-07 25828 70250 hsa-mir-10a MIR-10 ACCCUGU chr17 na na
Hsa-Mir-146-P1_5p -1.69 0.34 9.05e-06 351 891 hsa-mir-146a MIR-146 GAGAACU chr5 na na
Hsa-Mir-92-P1c_3p -1.32 0.26 4.00e-06 303 634 hsa-mir-92b MIR-92 AUUGCAC chr1 na na
Hsa-Mir-181-P1c_5p -1.25 0.21 6.34e-08 261 527 hsa-mir-181c MIR-181 ACAUUCA chr19 na na
Hsa-Mir-425_5p -1.16 0.19 6.36e-09 247 499 hsa-mir-425 MIR-425 AUGACAC chr3 na na
Hsa-Mir-362-P3_3p -1.15 0.36 2.88e-03 60 103 hsa-mir-501 MIR-362 AUGCACC chrX na na
Hsa-Mir-192-P1_5p -1.10 0.27 7.22e-05 109276 212293 hsa-mir-192 MIR-192 UGACCUA chr11 na na
Hsa-Mir-10-P2b_5p -1.08 0.36 6.81e-03 1478 2499 hsa-mir-99b MIR-10 ACCCGUA chr19 na na
Hsa-Mir-210_3p -1.07 0.28 1.53e-03 106 185 hsa-mir-210 MIR-210 UGUGCGU chr11 na na
Hsa-Mir-132-P1_3p -1.03 0.20 2.09e-06 61 117 hsa-mir-132 MIR-132 AACAGUC chr17 na na
Hsa-Mir-194-P1_5p/P2_5p -0.94 0.27 5.19e-04 6323 11619 hsa-mir-194-2 MIR-194 GUAACAG chr11 na na
Hsa-Mir-130-P4a_3p -0.84 0.13 2.77e-10 70 111 hsa-mir-454 MIR-130 AGUGCAA chr17 na na
Hsa-Mir-188-P2_5p -0.82 0.19 9.56e-05 279 427 hsa-mir-532 MIR-188 AUGCCUU chrX na na
Hsa-Mir-191_5p -0.78 0.23 1.91e-03 10721 14608 hsa-mir-191 MIR-191 AACGGAA chr3 na na
Hsa-Mir-21_5p -0.76 0.17 1.54e-04 19240 28173 hsa-mir-21 MIR-21 AGCUUAU chr17 na na
Hsa-Mir-130-P2a_3p -0.71 0.20 3.57e-03 92 131 hsa-mir-301a MIR-130 AGUGCAA chr17 na na
Hsa-Mir-1307_3p -0.66 0.19 1.89e-03 89 127 hsa-mir-1307 MIR-1307 CGACCGG chr10 na na
Hsa-Mir-362-P2_3p/P4_3p -0.61 0.20 7.29e-03 211 268 hsa-mir-500a MIR-362 UGCACCU chrX na na
Hsa-Mir-1307_5p -0.61 0.26 3.36e-02 502 713 hsa-mir-1307 MIR-1307 CGACCGG chr10 na na 
# Number of downregulated miRNA
signature_mirnas$number_downregulated
## [1] 44

nCR vs nLu

column='tissue.type'
tissue_type_A <- 'normal.lung'
tissue_type_B <- 'normal.colorect'
norm_adj_up       = "None"
norm_adj_down     = "None"
pCRC_adj_up   = "None"
pCRC_adj_down = "None"

coef <- paste(column, tissue_type_A, 'vs', tissue_type_B, sep='_')
res <- DeseqResult(dds, column, coef, tissue_type_A, tissue_type_B,
                   lfc.Threshold, rpm.Threshold,
                   norm_adj_up,
                   norm_adj_down)
dict_sig_mirna[paste(coef, "up",   sep='_')] <- list(res$up_mirna)
dict_sig_mirna[paste(coef, "down", sep='_')] <- list(res$down_mirna)
res_res <- res$res
res_dict[coef] <- res_res
plotMA(res$res, alpha=0.05)

# Plot volcano plot
VolcanoPlot(res$res, coef, res$sig,
            res$up_mirna, res$down_mirna,
            norm_adj_up, norm_adj_down,
            pCRC_adj_up, pCRC_adj_down)

ExpressionPlot(res$res, res$rpm, coef, res$sig,
               tissue_type_A, tissue_type_B,
               res$up_mirna, res$down_mirna,
               norm_adj_up, norm_adj_down,
               pCRC_adj_up, pCRC_adj_down)

signature_mirnas <- SigList(res, dds, tissue_type_A, tissue_type_B, coef,
                            norm_adj_up, norm_adj_down, 
                            pCRC_adj_up, pCRC_adj_down)
# Print list upregulated miRNA
signature_mirnas$up_mirna
Upregulated in tissue.type_normal.lung_vs_normal.colorect
miRNA LFC lfcSE FDR RPM normal.lung RPM normal.colorect miRBase_ID Family Seed Chr Cell-Type Specific Norm Background pCRC Background
Hsa-Mir-335_5p 1.48 0.21 3.73e-11 300 79 hsa-mir-335 MIR-335 CAAGAGC chr7 Retinal Epithelial Cell na na
Hsa-Mir-144_5p 2.32 0.41 5.46e-07 451 65 hsa-mir-144 MIR-144 GAUAUCA chr17 Red Blood Cell na na
Hsa-Mir-451_5p 1.90 0.47 1.06e-03 12496 2815 hsa-mir-451a MIR-451 AACCGUU chr17 Red Blood Cell na na
Hsa-Mir-24-P1_3p/P2_3p 0.86 0.25 1.88e-03 1084 413 hsa-mir-24-2 MIR-24 GGCUCAG chr19 Macrophage na na
Hsa-Mir-486_5p 2.04 0.45 1.33e-04 22081 3961 hsa-mir-486-1 MIR-486 CCUGUAC chr8 c(“Platelet”, “Red Blood Cell”) na na
Hsa-Mir-126_5p 2.57 0.26 4.06e-21 33620 4230 hsa-mir-126 MIR-126 AUUAUUA chr9 c(“Endothelial Cell”, “Platelet”) na na
Hsa-Mir-146-P2_5p 1.65 0.41 1.94e-04 17435 3259 hsa-mir-146b MIR-146 GAGAACU chr10 c(“Dendritic Cell”, “Lymphocyte”) na na
Hsa-Mir-342_3p 1.00 0.31 6.94e-03 681 260 hsa-mir-342 MIR-342 CUCACAC chr14 c(“Dendritic Cell”, “Lymphocyte”, “Macrophage”) na na
Hsa-Mir-34-P2b_5p 5.29 0.43 1.18e-33 748 11 hsa-mir-34c MIR-34 GGCAGUG chr11 na na
Hsa-Mir-34-P2a_5p 4.98 0.49 3.17e-22 116 2 hsa-mir-34b MIR-34 GGCAGUG chr11 na na
Hsa-Mir-184_3p 4.75 0.92 5.98e-06 111 1 hsa-mir-184 MIR-184 GGACGGA chr15 na na
Hsa-Mir-30-P1a_5p 3.04 0.28 6.17e-24 60860 5150 hsa-mir-30a MIR-30 GUAAACA chr6 na na
Hsa-Mir-218-P1_5p/P2_5p 2.39 0.33 7.14e-11 374 52 hsa-mir-218-1 MIR-218 UGUGCUU chr4 na na
Hsa-Mir-10-P2c_5p 2.23 0.41 4.40e-07 804 114 hsa-mir-99a MIR-10 ACCCGUA chr21 na na
Hsa-Mir-181-P1a_5p/P1b_5p 2.19 0.24 1.16e-18 80063 12321 hsa-mir-181a-1 MIR-181 ACAUUCA chr1 na na
Hsa-Mir-181-P2a_5p/P2b_5p 2.06 0.23 2.25e-18 3799 650 hsa-mir-181b-1 MIR-181 ACAUUCA chr1 na na
Hsa-Let-7-P1c_5p 1.97 0.35 1.57e-07 2606 466 hsa-let-7c LET-7 GAGGUAG chr21 na na
Hsa-Mir-130-P1a_3p 1.92 0.25 5.89e-13 1941 379 hsa-mir-130a MIR-130 AGUGCAA chr11 na na
Hsa-Mir-30-P1c_5p 1.74 0.20 4.37e-16 34088 7243 hsa-mir-30d MIR-30 GUAAACA chr8 na na
Hsa-Mir-101-P1_3p/P2_3p 1.45 0.22 1.42e-09 16084 4197 hsa-mir-101-1 MIR-101 UACAGUA chr1 na na
Hsa-Mir-338-P1_3p 1.45 0.39 2.15e-03 605 166 hsa-mir-338 MIR-338 CCAGCAU chr17 na na
Hsa-Mir-181-P2c_5p 1.42 0.28 1.14e-06 200 51 hsa-mir-181d MIR-181 ACAUUCA chr19 na na
Hsa-Mir-10-P2a_5p 1.40 0.40 2.08e-03 4056 986 hsa-mir-100 MIR-10 ACCCGUA chr11 na na
Hsa-Mir-10-P3a_5p 1.37 0.38 1.96e-03 507 137 hsa-mir-125b-1 MIR-10 CCCUGAG chr11 na na
Hsa-Mir-10-P3b_5p 1.34 0.34 7.55e-04 8561 2392 hsa-mir-125a MIR-10 CCCUGAG chr19 na na
Hsa-Mir-181-P1c_5p 1.25 0.26 5.14e-06 1790 527 hsa-mir-181c MIR-181 ACAUUCA chr19 na na
Hsa-Mir-140_3p 1.25 0.19 1.88e-09 2195 686 hsa-mir-140 MIR-140 CCACAGG chr16 na na
Hsa-Let-7-P2b1_5p 1.10 0.15 1.06e-11 6253 2110 hsa-let-7f-1 LET-7 GAGGUAG chr9 na na
Hsa-Mir-30-P2a_5p/P2b_5p/P2c_5p 1.08 0.19 9.04e-08 7603 2677 hsa-mir-30c-2 MIR-30 GUAAACA chr6 na na
Hsa-Mir-221-P1_3p 0.95 0.23 1.40e-04 2502 964 hsa-mir-221 MIR-221 GCUACAU chrX na na
Hsa-Mir-92-P1c_3p 0.90 0.32 1.56e-02 1737 634 hsa-mir-92b MIR-92 AUUGCAC chr1 na na
Hsa-Mir-15-P2c_5p 0.86 0.30 2.04e-02 1552 672 hsa-mir-195 MIR-15 AGCAGCA chr17 na na
Hsa-Mir-374-P2_5p 0.80 0.25 3.59e-03 148 65 hsa-mir-374b MIR-374 UAUAAUA chrX na na
Hsa-Mir-221-P2_3p 0.77 0.25 5.86e-03 1346 574 hsa-mir-222 MIR-221 GCUACAU chrX na na
Hsa-Let-7-P2c2_5p 0.76 0.21 1.16e-03 5088 2218 hsa-let-7i LET-7 GAGGUAG chr12 na na
Hsa-Mir-130-P2a_3p 0.75 0.25 6.48e-03 305 131 hsa-mir-301a MIR-130 AGUGCAA chr17 na na
Hsa-Mir-652_3p 0.71 0.20 1.38e-03 187 86 hsa-mir-652 MIR-652 AUGGCGC chrX na na
Hsa-Mir-26-P1_5p/P2_5p 0.68 0.17 3.54e-04 123947 56268 hsa-mir-26b MIR-26 UCAAGUA chr2 na na
Hsa-Mir-28-P2_3p 0.63 0.21 9.02e-03 6007 2638 hsa-mir-151a MIR-28 CGAGGAG chr8 na na 
# Number of upregulated miRNA
signature_mirnas$number_upregulated
## [1] 39
# Print list downregulated miRNA
signature_mirnas$down_mirna
Downregulated in tissue.type_normal.lung_vs_normal.colorect
miRNA LFC lfcSE FDR RPM normal.lung RPM normal.colorect miRBase_ID Family Seed Chr Cell-Type Specific Norm Background pCRC Background
Hsa-Mir-143_3p -0.81 0.34 2.00e-02 133600 164812 hsa-mir-143 MIR-143 GAGAUGA chr5 Mesenchymal na na
Hsa-Mir-192-P1_5p/P2_5p -8.51 0.40 3.64e-97 142 46010 hsa-mir-192 MIR-192 UGACCUA chr11 Epithelial Cell na na
Hsa-Mir-8-P2a_3p -4.00 0.29 6.74e-40 618 7741 hsa-mir-200b MIR-8 AAUACUG chr1 Epithelial Cell na na
Hsa-Mir-8-P2b_3p -2.06 0.29 1.04e-11 766 2444 hsa-mir-200c MIR-8 AAUACUG chr12 Epithelial Cell na na
Hsa-Mir-17-P1a_5p/P1b_5p -0.77 0.24 9.07e-03 184 225 hsa-mir-17 MIR-17 AAAGUGC chr13 CD14+ Monocyte na na
Hsa-Mir-133-P1_3p/P2_3p/P3_3p -2.33 0.47 9.21e-08 92 460 hsa-mir-133a-2 MIR-133 UUGGUCC chr20 c(“Skeletal Myocyte”, “Stem Cell”) na na
Hsa-Mir-375_3p -3.01 0.47 2.15e-10 2346 17215 hsa-mir-375 MIR-375 UUGUUCG chr2 c(“Epithelial Cell”, “Islet Cell”, “Neural”) na na
Hsa-Mir-194-P1_5p/P2_5p -7.89 0.34 2.93e-114 58 11619 hsa-mir-194-2 MIR-194 GUAACAG chr11 na na
Hsa-Mir-192-P1_5p -7.28 0.34 3.81e-95 1684 212293 hsa-mir-192 MIR-192 UGACCUA chr11 na na
Hsa-Mir-196-P1_5p/P2_5p -6.68 0.41 1.42e-54 3 250 hsa-mir-196a-1 MIR-196 AGGUAGU chr17 na na
Hsa-Mir-196-P3_5p -5.95 0.47 9.59e-33 7 400 hsa-mir-196b MIR-196 AGGUAGU chr7 na na
Hsa-Mir-577_5p -5.95 0.41 4.39e-45 3 138 hsa-mir-577 MIR-577 UAGAUAA chr4 na na
Hsa-Mir-147_3p -5.82 0.43 5.54e-40 2 110 hsa-mir-147b MIR-147 UGUGCGG chr15 na na
Hsa-Mir-190-P1_5p -4.03 0.32 1.35e-34 22 294 hsa-mir-190a MIR-190 GAUAUGU chr15 na na
Hsa-Mir-378_3p -3.17 0.24 2.10e-38 2189 14834 hsa-mir-378a MIR-378 CUGGACU chr5 na na
Hsa-Mir-127_3p -2.96 0.40 4.55e-12 327 1967 hsa-mir-127 MIR-127 CGGAUCC chr14 na na
Hsa-Mir-10-P1b_5p -2.67 0.45 5.72e-09 14125 75067 hsa-mir-10b MIR-10 ACCCUGU chr2 na na
Hsa-Mir-8-P1a_3p -2.66 0.29 3.55e-18 353 1754 hsa-mir-200a MIR-8 AACACUG chr1 na na
Hsa-Mir-8-P3a_3p -2.65 0.30 1.86e-17 262 1282 hsa-mir-429 MIR-8 AAUACUG chr1 na na
Hsa-Mir-154-P23_3p -1.79 0.25 3.73e-11 41 108 hsa-mir-654 MIR-154 AUGUCUG chr14 na na
Hsa-Mir-425_5p -1.66 0.23 3.12e-11 213 499 hsa-mir-425 MIR-425 AUGACAC chr3 na na
Hsa-Mir-154-P9_3p -1.37 0.26 6.98e-07 135 265 hsa-mir-381 MIR-154 AUACAAG chr14 na na
Hsa-Mir-154-P13_5p -1.35 0.27 3.42e-06 161 311 hsa-mir-411 MIR-154 AGUAGAC chr14 na na
Hsa-Mir-28-P1_3p -1.24 0.23 3.62e-07 2385 3956 hsa-mir-28 MIR-28 ACUAGAU chr3 na na
Hsa-Mir-17-P3a_5p -1.23 0.29 2.53e-04 194 322 hsa-mir-20a MIR-17 AAAGUGC chr13 na na
Hsa-Mir-1307_3p -1.09 0.24 2.47e-05 80 127 hsa-mir-1307 MIR-1307 CGACCGG chr10 na na
Hsa-Mir-8-P1b_3p -1.06 0.38 1.07e-02 6145 9180 hsa-mir-141 MIR-8 AACACUG chr12 na na
Hsa-Mir-1307_5p -1.03 0.32 4.10e-03 444 713 hsa-mir-1307 MIR-1307 CGACCGG chr10 na na
Hsa-Mir-362-P3_3p -1.00 0.45 4.09e-02 82 103 hsa-mir-501 MIR-362 AUGCACC chrX na na
Hsa-Mir-210_3p -0.93 0.35 3.36e-02 138 185 hsa-mir-210 MIR-210 UGUGCGU chr11 na na
Hsa-Mir-136_3p -0.89 0.27 2.95e-03 88 127 hsa-mir-136 MIR-136 AUCAUCG chr14 na na
Hsa-Mir-21_5p -0.86 0.22 6.21e-04 21349 28173 hsa-mir-21 MIR-21 AGCUUAU chr17 na na
Hsa-Mir-30-P1b_5p -0.86 0.18 8.68e-06 4513 6046 hsa-mir-30e MIR-30 GUAAACA chr1 na na
Hsa-Mir-191_5p -0.82 0.28 1.02e-02 12597 14608 hsa-mir-191 MIR-191 AACGGAA chr3 na na
Hsa-Mir-148-P1_3p -0.72 0.30 4.78e-02 18874 21730 hsa-mir-148a MIR-148 CAGUGCA chr7 na na 
# Number of downregulated miRNA
signature_mirnas$number_downregulated
## [1] 35

nCR vs pCRC

column='tissue.type'
tissue_type_A <- 'tumor.colorect'
tissue_type_B <- 'normal.colorect'
norm_adj_up       = "None"
norm_adj_down     = "None"
pCRC_adj_up   = "None"
pCRC_adj_down = "None"

coef <- paste(column, tissue_type_A, 'vs', tissue_type_B, sep='_')
res <- DeseqResult(dds, column, coef, tissue_type_A, tissue_type_B,
                   lfc.Threshold, rpm.Threshold,
                   norm_adj_up,
                   norm_adj_down)
dict_sig_mirna[paste(coef, "up",   sep='_')] <- list(res$up_mirna)
dict_sig_mirna[paste(coef, "down", sep='_')] <- list(res$down_mirna)
res_res <- res$res
res_dict[coef] <- res_res
plotMA(res$res, alpha=0.05)

# Plot volcano plot
VolcanoPlot(res$res, coef, res$sig,
            res$up_mirna, res$down_mirna,
            norm_adj_up, norm_adj_down,
            pCRC_adj_up, pCRC_adj_down)

ExpressionPlot(res$res, res$rpm, coef, res$sig,
               tissue_type_A, tissue_type_B,
               res$up_mirna, res$down_mirna,
               norm_adj_up, norm_adj_down,
               pCRC_adj_up, pCRC_adj_down)

signature_mirnas <- SigList(res, dds, tissue_type_A, tissue_type_B, coef,
                            norm_adj_up, norm_adj_down, 
                            pCRC_adj_up, pCRC_adj_down)
# Print list upregulated miRNA
signature_mirnas$up_mirna
Upregulated in tissue.type_tumor.colorect_vs_normal.colorect
miRNA LFC lfcSE FDR RPM tumor.colorect RPM normal.colorect miRBase_ID Family Seed Chr Cell-Type Specific Norm Background pCRC Background
Hsa-Mir-17-P1a_5p/P1b_5p 1.42 0.14 1.30e-22 900 225 hsa-mir-17 MIR-17 AAAGUGC chr13 CD14+ Monocyte na na
Hsa-Mir-7-P1_5p/P2_5p/P3_5p 2.32 0.25 8.96e-18 199 22 hsa-mir-7-1 MIR-7 GGAAGAC chr9 c(“Islet Cell”, “Neural”) na na
Hsa-Mir-223_3p 1.22 0.24 2.50e-06 619 177 hsa-mir-223 MIR-223 GUCAGUU chrX c(“Dendritic Cell”, “Macrophage”) na na
Hsa-Mir-31_5p 4.32 0.33 1.40e-44 627 6 hsa-mir-31 MIR-31 GGCAAGA chr9 na na
Hsa-Mir-135-P3_5p 4.10 0.23 1.68e-69 155 4 hsa-mir-135b MIR-135 AUGGCUU chr1 na na
Hsa-Mir-224_5p 2.48 0.19 2.72e-36 395 43 hsa-mir-224 MIR-224 AAGUCAC chrX na na
Hsa-Mir-584_5p 1.94 0.21 2.16e-19 110 17 hsa-mir-584 MIR-584 UAUGGUU chr5 na na
Hsa-Mir-15-P1d_5p 1.87 0.21 1.15e-17 235 40 hsa-mir-424 MIR-15 AGCAGCA chrX na na
Hsa-Mir-96-P2_5p 1.84 0.17 6.44e-24 10417 1867 hsa-mir-182 MIR-96 UUGGCAA chr7 na na
Hsa-Mir-17-P3a_5p 1.62 0.16 2.85e-21 1513 322 hsa-mir-20a MIR-17 AAAGUGC chr13 na na
Hsa-Mir-96-P3_5p 1.53 0.23 1.96e-10 1063 224 hsa-mir-183 MIR-96 AUGGCAC chr7 na na
Hsa-Mir-19-P1_3p 1.47 0.17 1.43e-17 379 87 hsa-mir-19a MIR-19 GUGCAAA chr13 na na
Hsa-Mir-21_5p 1.35 0.13 1.01e-24 105735 28173 hsa-mir-21 MIR-21 AGCUUAU chr17 na na
Hsa-Mir-17-P2a_5p 1.33 0.19 6.62e-11 168 44 hsa-mir-18a MIR-17 AAGGUGC chr13 na na
Hsa-Mir-181-P2c_5p 1.33 0.16 1.40e-15 196 51 hsa-mir-181d MIR-181 ACAUUCA chr19 na na
Hsa-Mir-95-P2_3p 1.17 0.14 1.40e-15 142 41 hsa-mir-421 MIR-95 UCAACAG chrX na na
Hsa-Mir-19-P2a_3p/P2b_3p 1.14 0.16 1.59e-12 1297 389 hsa-mir-19b-1 MIR-19 GUGCAAA chr13 na na
Hsa-Mir-181-P1c_5p 1.11 0.15 1.06e-12 1724 527 hsa-mir-181c MIR-181 ACAUUCA chr19 na na
Hsa-Mir-29-P2a_3p/P2b_3p 1.08 0.17 5.43e-10 380 124 hsa-mir-29b-1 MIR-29 AGCACCA chr7 na na
Hsa-Mir-130-P2a_3p 1.05 0.15 8.09e-12 409 131 hsa-mir-301a MIR-130 AGUGCAA chr17 na na
Hsa-Mir-17-P3c_5p 0.97 0.12 1.00e-15 370 130 hsa-mir-106b MIR-17 AAAGUGC chr7 na na
Hsa-Mir-221-P2_3p 0.94 0.15 1.32e-09 1633 574 hsa-mir-222 MIR-221 GCUACAU chrX na na
Hsa-Mir-92-P1a_3p/P1b_3p 0.90 0.16 5.66e-08 39320 13849 hsa-mir-92a-1 MIR-92 AUUGCAC chr13 na na
Hsa-Mir-17-P1c_5p 0.88 0.10 2.69e-17 2287 843 hsa-mir-93 MIR-17 AAAGUGC chr7 na na
Hsa-Mir-221-P1_3p 0.83 0.13 4.23e-09 2529 964 hsa-mir-221 MIR-221 GCUACAU chrX na na
Hsa-Mir-203_3p 0.74 0.20 9.13e-04 2307 922 hsa-mir-203a MIR-203 UGAAAUG chr14 na na
Hsa-Let-7-P2c2_5p 0.74 0.12 1.50e-08 5432 2218 hsa-let-7i LET-7 GAGGUAG chr12 na na
Hsa-Let-7-P2b3_5p 0.68 0.14 4.33e-06 2149 937 hsa-mir-98 LET-7 GAGGUAG chrX na na
Hsa-Mir-29-P1a_3p 0.62 0.12 1.40e-06 3667 1692 hsa-mir-29a MIR-29 AGCACCA chr7 na na
Hsa-Mir-92-P2c_3p 0.62 0.10 4.70e-09 3274 1443 hsa-mir-25 MIR-92 AUUGCAC chr7 na na
Hsa-Mir-92-P1c_3p 0.60 0.18 4.90e-03 1398 634 hsa-mir-92b MIR-92 AUUGCAC chr1 na na
Hsa-Mir-769_5p 0.59 0.11 1.18e-06 439 195 hsa-mir-769 MIR-769 GAGACCU chr19 na na 
# Number of upregulated miRNA
signature_mirnas$number_upregulated
## [1] 32
# Print list downregulated miRNA
signature_mirnas$down_mirna
Downregulated in tissue.type_tumor.colorect_vs_normal.colorect
miRNA LFC lfcSE FDR RPM tumor.colorect RPM normal.colorect miRBase_ID Family Seed Chr Cell-Type Specific Norm Background pCRC Background
Hsa-Mir-451_5p -1.19 0.26 3.69e-05 1565 2815 hsa-mir-451a MIR-451 AACCGUU chr17 Red Blood Cell na na
Hsa-Mir-145_5p -1.97 0.22 1.63e-17 840 2832 hsa-mir-145 MIR-145 UCCAGUU chr5 Mesenchymal na na
Hsa-Mir-143_3p -0.60 0.19 1.39e-03 148431 164812 hsa-mir-143 MIR-143 GAGAUGA chr5 Mesenchymal na na
Hsa-Mir-150_5p -1.36 0.24 3.58e-07 280 586 hsa-mir-150 MIR-150 CUCCCAA chr19 Lymphocyte na na
Hsa-Mir-192-P1_5p/P2_5p -2.05 0.22 3.04e-19 13717 46010 hsa-mir-192 MIR-192 UGACCUA chr11 Epithelial Cell na na
Hsa-Mir-133-P1_3p/P2_3p/P3_3p -1.98 0.26 6.98e-16 116 460 hsa-mir-133a-2 MIR-133 UUGGUCC chr20 c(“Skeletal Myocyte”, “Stem Cell”) na na
Hsa-Mir-486_5p -1.18 0.25 2.02e-05 2013 3961 hsa-mir-486-1 MIR-486 CCUGUAC chr8 c(“Platelet”, “Red Blood Cell”) na na
Hsa-Mir-375_3p -1.86 0.26 2.92e-12 5263 17215 hsa-mir-375 MIR-375 UUGUUCG chr2 c(“Epithelial Cell”, “Islet Cell”, “Neural”) na na
Hsa-Mir-126_5p -0.72 0.15 1.61e-05 3455 4230 hsa-mir-126 MIR-126 AUUAUUA chr9 c(“Endothelial Cell”, “Platelet”) na na
Hsa-Mir-342_3p -1.26 0.18 1.59e-10 145 260 hsa-mir-342 MIR-342 CUCACAC chr14 c(“Dendritic Cell”, “Lymphocyte”, “Macrophage”) na na
Hsa-Mir-147_3p -1.92 0.23 8.04e-17 33 110 hsa-mir-147b MIR-147 UGUGCGG chr15 na na
Hsa-Mir-15-P2c_5p -1.76 0.17 6.42e-23 257 672 hsa-mir-195 MIR-15 AGCAGCA chr17 na na
Hsa-Mir-378_3p -1.72 0.14 8.99e-34 6426 14834 hsa-mir-378a MIR-378 CUGGACU chr5 na na
Hsa-Mir-15-P1c_5p -1.62 0.16 5.86e-22 143 337 hsa-mir-497 MIR-15 AGCAGCA chr17 na na
Hsa-Mir-190-P1_5p -1.47 0.18 1.43e-15 140 294 hsa-mir-190a MIR-190 GAUAUGU chr15 na na
Hsa-Mir-194-P1_5p/P2_5p -1.31 0.19 3.24e-11 6167 11619 hsa-mir-194-2 MIR-194 GUAACAG chr11 na na
Hsa-Mir-30-P1a_5p -1.21 0.16 7.30e-12 3165 5150 hsa-mir-30a MIR-30 GUAAACA chr6 na na
Hsa-Mir-26-P3_5p -1.21 0.14 3.12e-16 3467 5898 hsa-mir-26a-1 MIR-26 UCAAGUA chr3 na na
Hsa-Mir-338-P1_3p -1.18 0.22 8.59e-07 98 166 hsa-mir-338 MIR-338 CCAGCAU chr17 na na
Hsa-Mir-26-P1_5p/P2_5p -1.15 0.10 4.50e-29 35584 56268 hsa-mir-26b MIR-26 UCAAGUA chr2 na na
Hsa-Mir-10-P1b_5p -1.11 0.25 4.63e-06 39286 75067 hsa-mir-10b MIR-10 ACCCUGU chr2 na na
Hsa-Mir-192-P1_5p -1.11 0.20 2.37e-08 132000 212293 hsa-mir-192 MIR-192 UGACCUA chr11 na na
Hsa-Mir-148-P2_3p -1.04 0.21 5.54e-06 137 210 hsa-mir-152 MIR-148 CAGUGCA chr17 na na
Hsa-Mir-15-P2a_5p/P2b_5p -1.02 0.12 8.21e-15 4522 6926 hsa-mir-16-1 MIR-15 AGCAGCA chr13 na na
Hsa-Mir-10-P3b_5p -0.97 0.19 4.09e-06 1649 2392 hsa-mir-125a MIR-10 CCCUGAG chr19 na na
Hsa-Mir-28-P1_3p -0.95 0.13 6.44e-12 2915 3956 hsa-mir-28 MIR-28 ACUAGAU chr3 na na
Hsa-Mir-1307_5p -0.95 0.18 2.27e-06 492 713 hsa-mir-1307 MIR-1307 CGACCGG chr10 na na
Hsa-Mir-29-P1b_3p -0.93 0.15 1.11e-09 331 464 hsa-mir-29c MIR-29 AGCACCA chr1 na na
Hsa-Mir-10-P3c_5p -0.80 0.21 1.44e-03 239 326 hsa-mir-125b-2 MIR-10 CCCUGAG chr21 na na
Hsa-Mir-574_3p -0.78 0.14 1.83e-07 240 305 hsa-mir-574 MIR-574 ACGCUCA chr4 na na
Hsa-Mir-8-P1b_3p -0.69 0.21 2.87e-03 7518 9180 hsa-mir-141 MIR-8 AACACUG chr12 na na
Hsa-Mir-191_5p -0.68 0.16 1.46e-04 13630 14608 hsa-mir-191 MIR-191 AACGGAA chr3 na na
Hsa-Mir-362-P3_3p -0.62 0.25 1.92e-02 108 103 hsa-mir-501 MIR-362 AUGCACC chrX na na
Hsa-Mir-142_5p -0.60 0.19 4.90e-03 3453 3817 hsa-mir-142 MIR-142 AUAAAGU chr17 na na
Hsa-Mir-154-P9_3p -0.59 0.15 1.81e-04 253 265 hsa-mir-381 MIR-154 AUACAAG chr14 na na 
# Number of downregulated miRNA
signature_mirnas$number_downregulated
## [1] 35
ref <- 'tumor.colorect'
dds <- DeseqObject(design, countdata, sampleinfo, "None", "None", ref)
#
# #datasets in total
dim(dds[, colData(dds)$type.tissue == 'pCRC'])
## [1] 389 120
dim(dds[, colData(dds)$type.tissue == 'mLi'])
## [1] 389  35
dim(dds[, colData(dds)$type.tissue == 'mLu'])
## [1] 389  28
dim(dds[, colData(dds)$type.tissue == 'nCR'])
## [1] 389  25
dim(dds[, colData(dds)$type.tissue == 'nLi'])
## [1] 389  20
dim(dds[, colData(dds)$type.tissue == 'nLu'])
## [1] 389  10
dim(dds[, colData(dds)$type.tissue == 'PM'])
## [1] 389  30
# #datasets for Fromm
dim(dds[, colData(dds)$type.tissue == 'pCRC' & colData(dds)$paper == 'fromm'])
## [1] 389   3
dim(dds[, colData(dds)$type.tissue == 'mLi' & colData(dds)$paper == 'fromm'])
## [1] 389  19
dim(dds[, colData(dds)$type.tissue == 'mLu' & colData(dds)$paper == 'fromm'])
## [1] 389  24
dim(dds[, colData(dds)$type.tissue == 'nCR' & colData(dds)$paper == 'fromm'])
## [1] 389   3
dim(dds[, colData(dds)$type.tissue == 'nLi' & colData(dds)$paper == 'fromm'])
## [1] 389   8
dim(dds[, colData(dds)$type.tissue == 'nLu' & colData(dds)$paper == 'fromm'])
## [1] 389   7
dim(dds[, colData(dds)$type.tissue == 'PM' & colData(dds)$paper == 'fromm'])
## [1] 389  18
# #datasets for Schee
dim(dds[, colData(dds)$type.tissue == 'pCRC' & colData(dds)$paper == 'schee'])
## [1] 389  83
dim(dds[, colData(dds)$type.tissue == 'mLi' & colData(dds)$paper == 'schee'])
## [1] 389   0
dim(dds[, colData(dds)$type.tissue == 'mLu' & colData(dds)$paper == 'schee'])
## [1] 389   0
dim(dds[, colData(dds)$type.tissue == 'nCR' & colData(dds)$paper == 'schee'])
## [1] 389   0
dim(dds[, colData(dds)$type.tissue == 'nLi' & colData(dds)$paper == 'schee'])
## [1] 389   0
dim(dds[, colData(dds)$type.tissue == 'nLu' & colData(dds)$paper == 'schee'])
## [1] 389   0
dim(dds[, colData(dds)$type.tissue == 'PM' & colData(dds)$paper == 'schee'])
## [1] 389   0
# #datasets for Schee
dim(dds[, colData(dds)$type.tissue == 'pCRC' & colData(dds)$paper == 'neerincx'])
## [1] 389  34
dim(dds[, colData(dds)$type.tissue == 'mLi' & colData(dds)$paper == 'neerincx'])
## [1] 389  16
dim(dds[, colData(dds)$type.tissue == 'mLu' & colData(dds)$paper == 'neerincx'])
## [1] 389   4
dim(dds[, colData(dds)$type.tissue == 'nCR' & colData(dds)$paper == 'neerincx'])
## [1] 389  22
dim(dds[, colData(dds)$type.tissue == 'nLi' & colData(dds)$paper == 'neerincx'])
## [1] 389   9
dim(dds[, colData(dds)$type.tissue == 'nLu' & colData(dds)$paper == 'neerincx'])
## [1] 389   3
dim(dds[, colData(dds)$type.tissue == 'PM' & colData(dds)$paper == 'neerincx'])
## [1] 389  12
# #datasets for Schee
dim(dds[, colData(dds)$type.tissue == 'pCRC' & colData(dds)$paper == 'selitsky'])
## [1] 389   0
dim(dds[, colData(dds)$type.tissue == 'mLi' & colData(dds)$paper == 'selitsky'])
## [1] 389   0
dim(dds[, colData(dds)$type.tissue == 'mLu' & colData(dds)$paper == 'selitsky'])
## [1] 389   0
dim(dds[, colData(dds)$type.tissue == 'nCR' & colData(dds)$paper == 'selitsky'])
## [1] 389   0
dim(dds[, colData(dds)$type.tissue == 'nLi' & colData(dds)$paper == 'selitsky'])
## [1] 389   3
dim(dds[, colData(dds)$type.tissue == 'nLu' & colData(dds)$paper == 'selitsky'])
## [1] 389   0
dim(dds[, colData(dds)$type.tissue == 'PM' & colData(dds)$paper == 'selitsky'])
## [1] 389   0
plotDispEsts(dds)

## pCRC vs nLi

column='tissue.type'
tissue_type_A <- 'normal.liver'
tissue_type_B <- 'tumor.colorect'
norm_adj_up       = "None"
norm_adj_down     = "None"
pCRC_adj_up   = "None"
pCRC_adj_down = "None"

coef <- paste(column, tissue_type_A, 'vs', tissue_type_B, sep='_')
res <- DeseqResult(dds, column, coef, tissue_type_A, tissue_type_B,
                   lfc.Threshold, rpm.Threshold,
                   norm_adj_up,
                   norm_adj_down)
dict_sig_mirna[paste(coef, "up",   sep='_')] <- list(res$up_mirna)
dict_sig_mirna[paste(coef, "down", sep='_')] <- list(res$down_mirna)
res_res <- res$res
res_dict[coef] <- res_res
plotMA(res$res, alpha=0.05)

# Plot volcano plot
VolcanoPlot(res$res, coef, res$sig,
            res$up_mirna, res$down_mirna,
            norm_adj_up, norm_adj_down,
            pCRC_adj_up, pCRC_adj_down)

ExpressionPlot(res$res, res$rpm, coef, res$sig,
               tissue_type_A, tissue_type_B,
               res$up_mirna, res$down_mirna,
               norm_adj_up, norm_adj_down,
               pCRC_adj_up, pCRC_adj_down)

signature_mirnas <- SigList(res, dds, tissue_type_A, tissue_type_B, coef,
                            norm_adj_up, norm_adj_down, 
                            pCRC_adj_up, pCRC_adj_down)
# Print list upregulated miRNA
signature_mirnas$up_mirna
Upregulated in tissue.type_normal.liver_vs_tumor.colorect
miRNA LFC lfcSE FDR RPM normal.liver RPM tumor.colorect miRBase_ID Family Seed Chr Cell-Type Specific Norm Background pCRC Background
Hsa-Mir-204-P1_5p 1.03 0.46 1.25e-04 190 62 hsa-mir-204 MIR-204 UCCCUUU chr9 Retinal Epithelial Cell na na
Hsa-Mir-335_5p 0.81 0.14 1.89e-08 205 150 hsa-mir-335 MIR-335 CAAGAGC chr7 Retinal Epithelial Cell na na
Hsa-Mir-144_5p 2.22 0.28 3.68e-15 206 57 hsa-mir-144 MIR-144 GAUAUCA chr17 Red Blood Cell na na
Hsa-Mir-451_5p 1.48 0.32 7.08e-06 3358 1565 hsa-mir-451a MIR-451 AACCGUU chr17 Red Blood Cell na na
Hsa-Mir-150_5p 2.07 0.29 5.24e-13 1053 280 hsa-mir-150 MIR-150 CUCCCAA chr19 Lymphocyte na na
Hsa-Mir-122_5p 11.53 0.78 4.04e-55 148842 9 hsa-mir-122 MIR-122 GGAGUGU chr18 Hepatocyte na na
Hsa-Mir-192-P1_5p/P2_5p 1.31 0.27 2.62e-06 29546 13717 hsa-mir-192 MIR-192 UGACCUA chr11 Epithelial Cell na na
Hsa-Mir-15-P1a_5p 0.62 0.12 1.17e-06 555 453 hsa-mir-15a MIR-15 AGCAGCA chr13 CD14+ Monocyte na na
Hsa-Mir-486_5p 2.48 0.30 8.95e-16 10617 2013 hsa-mir-486-1 MIR-486 CCUGUAC chr8 c(“Platelet”, “Red Blood Cell”) na na
Hsa-Mir-126_5p 1.95 0.17 7.32e-30 11148 3455 hsa-mir-126 MIR-126 AUUAUUA chr9 c(“Endothelial Cell”, “Platelet”) na na
Hsa-Mir-342_3p 1.05 0.21 5.60e-07 254 145 hsa-mir-342 MIR-342 CUCACAC chr14 c(“Dendritic Cell”, “Lymphocyte”, “Macrophage”) na na
Hsa-Mir-885_5p 9.48 0.68 8.47e-41 510 0 hsa-mir-885 MIR-885 CCAUUAC chr3 na na
Hsa-Mir-139_5p 4.20 0.21 6.49e-84 174 11 hsa-mir-139 MIR-139 CUACAGU chr11 na na
Hsa-Let-7-P1c_5p 3.25 0.23 3.14e-43 4945 606 hsa-let-7c LET-7 GAGGUAG chr21 na na
Hsa-Mir-10-P2c_5p 3.24 0.28 2.36e-31 1202 142 hsa-mir-99a MIR-10 ACCCGUA chr21 na na
Hsa-Mir-10-P3c_5p 2.87 0.25 2.44e-30 1620 239 hsa-mir-125b-2 MIR-10 CCCUGAG chr21 na na
Hsa-Mir-30-P1a_5p 2.51 0.19 3.24e-40 15175 3165 hsa-mir-30a MIR-30 GUAAACA chr6 na na
Hsa-Mir-193-P1a_5p 2.48 0.23 3.82e-25 298 65 hsa-mir-193a MIR-193 GGGUCUU chr17 na na
Hsa-Mir-193-P2a_3p/P2b_3p 2.33 0.19 1.61e-32 219 56 hsa-mir-365b MIR-193 AAUGCCC chr17 na na
Hsa-Mir-10-P3a_5p 2.29 0.26 3.08e-18 611 145 hsa-mir-125b-1 MIR-10 CCCUGAG chr11 na na
Hsa-Mir-193-P1b_3p 2.17 0.19 2.30e-29 514 145 hsa-mir-193b MIR-193 ACUGGCC chr16 na na
Hsa-Mir-455_5p 2.10 0.15 6.57e-45 279 83 hsa-mir-455 MIR-455 AUGUGCC chr9 na na
Hsa-Mir-574_3p 1.99 0.16 1.44e-34 745 240 hsa-mir-574 MIR-574 ACGCUCA chr4 na na
Hsa-Mir-101-P1_3p/P2_3p 1.98 0.15 6.40e-41 14649 4638 hsa-mir-101-1 MIR-101 UACAGUA chr1 na na
Hsa-Mir-148-P1_3p 1.86 0.20 1.75e-19 105086 34704 hsa-mir-148a MIR-148 CAGUGCA chr7 na na
Hsa-Mir-423_5p 1.83 0.20 1.11e-18 1198 433 hsa-mir-423 MIR-423 GAGGGGC chr17 na na
Hsa-Mir-22-P1a_3p 1.79 0.14 1.08e-36 78932 29018 hsa-mir-22 MIR-22 AGCUGCC chr17 na na
Hsa-Mir-744_5p 1.79 0.17 1.28e-23 165 61 hsa-mir-744 MIR-744 GCGGGGC chr17 na na
Hsa-Mir-30-P2a_5p/P2b_5p/P2c_5p 1.45 0.12 7.16e-32 5891 2793 hsa-mir-30c-2 MIR-30 GUAAACA chr6 na na
Hsa-Mir-130-P1a_3p 1.43 0.16 1.39e-17 907 421 hsa-mir-130a MIR-130 AGUGCAA chr11 na na
Hsa-Mir-378_3p 1.35 0.16 1.07e-16 12736 6426 hsa-mir-378a MIR-378 CUGGACU chr5 na na
Hsa-Mir-148-P2_3p 1.34 0.25 1.57e-07 289 137 hsa-mir-152 MIR-148 CAGUGCA chr17 na na
Hsa-Mir-197_3p 1.28 0.15 5.68e-16 344 181 hsa-mir-197 MIR-197 UCACCAC chr1 na na
Hsa-Mir-15-P1c_5p 1.25 0.19 3.90e-11 276 143 hsa-mir-497 MIR-15 AGCAGCA chr17 na na
Hsa-Mir-26-P3_5p 1.24 0.16 2.22e-14 6787 3467 hsa-mir-26a-1 MIR-26 UCAAGUA chr3 na na
Hsa-Mir-15-P2c_5p 1.19 0.20 1.78e-09 496 257 hsa-mir-195 MIR-15 AGCAGCA chr17 na na
Hsa-Mir-331_3p 1.16 0.21 6.75e-08 110 60 hsa-mir-331 MIR-331 CCCCUGG chr12 na na
Hsa-Mir-26-P1_5p/P2_5p 1.10 0.11 1.13e-22 62274 35584 hsa-mir-26b MIR-26 UCAAGUA chr2 na na
Hsa-Mir-10-P3b_5p 1.09 0.23 2.99e-06 3040 1649 hsa-mir-125a MIR-10 CCCUGAG chr19 na na
Hsa-Mir-30-P1b_5p 1.08 0.12 5.44e-19 11240 6833 hsa-mir-30e MIR-30 GUAAACA chr1 na na
Hsa-Mir-340_5p 1.05 0.13 1.20e-15 1030 657 hsa-mir-340 MIR-340 UAUAAAG chr5 na na
Hsa-Mir-27-P1_3p/P2_3p 1.04 0.12 4.13e-17 50009 29444 hsa-mir-27a MIR-27 UCACAGU chr19 na na
Hsa-Mir-10-P2a_5p 1.00 0.27 4.80e-04 2634 1724 hsa-mir-100 MIR-10 ACCCGUA chr11 na na
Hsa-Mir-29-P1b_3p 0.98 0.17 1.55e-08 516 331 hsa-mir-29c MIR-29 AGCACCA chr1 na na
Hsa-Mir-154-P23_3p 0.94 0.16 3.85e-08 222 148 hsa-mir-654 MIR-154 AUGUCUG chr14 na na
Hsa-Mir-15-P2a_5p/P2b_5p 0.87 0.14 9.10e-10 6884 4522 hsa-mir-16-1 MIR-15 AGCAGCA chr13 na na
Hsa-Mir-30-P1c_5p 0.84 0.13 9.49e-10 13680 9703 hsa-mir-30d MIR-30 GUAAACA chr8 na na
Hsa-Mir-154-P13_5p 0.78 0.18 2.94e-05 442 328 hsa-mir-411 MIR-154 AGUAGAC chr14 na na
Hsa-Mir-28-P2_5p 0.76 0.15 1.01e-06 3098 2244 hsa-mir-151a MIR-28 CGAGGAG chr8 na na
Hsa-Mir-92-P1a_3p/P1b_3p 0.71 0.18 2.33e-04 51173 39320 hsa-mir-92a-1 MIR-92 AUUGCAC chr13 na na
Hsa-Mir-136_3p 0.65 0.18 4.46e-04 208 173 hsa-mir-136 MIR-136 AUCAUCG chr14 na na 
# Number of upregulated miRNA
signature_mirnas$number_upregulated
## [1] 51
# Print list downregulated miRNA
signature_mirnas$down_mirna
Downregulated in tissue.type_normal.liver_vs_tumor.colorect
miRNA LFC lfcSE FDR RPM normal.liver RPM tumor.colorect miRBase_ID Family Seed Chr Cell-Type Specific Norm Background pCRC Background
Hsa-Mir-143_3p -1.72 0.23 4.38e-14 37572 148431 hsa-mir-143 MIR-143 GAGAUGA chr5 Mesenchymal na na
Hsa-Mir-24-P1_3p/P2_3p -0.77 0.16 8.36e-06 319 626 hsa-mir-24-2 MIR-24 GGCUCAG chr19 Macrophage na na
Hsa-Mir-8-P2b_3p -6.07 0.19 3.90e-210 31 2727 hsa-mir-200c MIR-8 AAUACUG chr12 Epithelial Cell na na
Hsa-Mir-8-P2a_3p -4.36 0.19 2.71e-109 369 10330 hsa-mir-200b MIR-8 AAUACUG chr1 Epithelial Cell na na
Hsa-Mir-17-P1a_5p/P1b_5p -1.32 0.16 4.02e-16 278 900 hsa-mir-17 MIR-17 AAAGUGC chr13 CD14+ Monocyte na na
Hsa-Mir-15-P1b_5p -0.92 0.13 1.30e-11 114 273 hsa-mir-15b MIR-15 AGCAGCA chr3 CD14+ Monocyte na na
Hsa-Mir-133-P1_3p/P2_3p/P3_3p -2.03 0.32 1.91e-10 22 116 hsa-mir-133a-2 MIR-133 UUGGUCC chr20 c(“Skeletal Myocyte”, “Stem Cell”) na na
Hsa-Mir-155_5p -1.44 0.21 1.42e-10 113 387 hsa-mir-155 MIR-155 UAAUGCU chr21 c(“Lymphocyte”, “Macrophage”) na na
Hsa-Mir-7-P1_5p/P2_5p/P3_5p -5.51 0.32 2.03e-63 3 199 hsa-mir-7-1 MIR-7 GGAAGAC chr9 c(“Islet Cell”, “Neural”) na na
Hsa-Mir-375_3p -0.84 0.32 2.21e-02 2553 5263 hsa-mir-375 MIR-375 UUGUUCG chr2 c(“Epithelial Cell”, “Islet Cell”, “Neural”) na na
Hsa-Mir-196-P1_5p/P2_5p -6.71 0.30 2.31e-108 2 319 hsa-mir-196a-1 MIR-196 AGGUAGU chr17 na na
Hsa-Mir-135-P3_5p -6.12 0.31 3.71e-82 2 155 hsa-mir-135b MIR-135 AUGGCUU chr1 na na
Hsa-Mir-196-P3_5p -6.11 0.32 1.17e-77 9 828 hsa-mir-196b MIR-196 AGGUAGU chr7 na na
Hsa-Mir-31_5p -5.76 0.45 3.20e-37 7 627 hsa-mir-31 MIR-31 GGCAAGA chr9 na na
Hsa-Mir-577_5p -5.70 0.29 1.37e-80 2 142 hsa-mir-577 MIR-577 UAGAUAA chr4 na na
Hsa-Mir-8-P1b_3p -5.35 0.25 1.29e-93 154 7518 hsa-mir-141 MIR-8 AACACUG chr12 na na
Hsa-Mir-96-P3_5p -5.08 0.27 2.84e-74 27 1063 hsa-mir-183 MIR-96 AUGGCAC chr7 na na
Hsa-Mir-8-P3a_3p -4.60 0.20 5.55e-118 68 2303 hsa-mir-429 MIR-8 AAUACUG chr1 na na
Hsa-Mir-96-P2_5p -4.47 0.20 4.46e-107 370 10417 hsa-mir-182 MIR-96 UUGGCAA chr7 na na
Hsa-Mir-10-P1b_5p -3.68 0.31 1.37e-31 2704 39286 hsa-mir-10b MIR-10 ACCCUGU chr2 na na
Hsa-Mir-8-P1a_3p -3.64 0.19 9.42e-76 108 1772 hsa-mir-200a MIR-8 AACACUG chr1 na na
Hsa-Mir-203_3p -3.42 0.24 2.77e-45 171 2307 hsa-mir-203a MIR-203 UGAAAUG chr14 na na
Hsa-Mir-221-P1_3p -3.13 0.15 5.83e-92 217 2529 hsa-mir-221 MIR-221 GCUACAU chrX na na
Hsa-Mir-224_5p -2.99 0.22 2.60e-39 36 395 hsa-mir-224 MIR-224 AAGUCAC chrX na na
Hsa-Mir-221-P2_3p -2.81 0.17 1.32e-61 173 1633 hsa-mir-222 MIR-221 GCUACAU chrX na na
Hsa-Mir-181-P2c_5p -2.59 0.19 7.58e-42 26 196 hsa-mir-181d MIR-181 ACAUUCA chr19 na na
Hsa-Mir-181-P1c_5p -2.35 0.17 3.48e-42 261 1724 hsa-mir-181c MIR-181 ACAUUCA chr19 na na
Hsa-Mir-584_5p -2.25 0.25 4.29e-18 18 110 hsa-mir-584 MIR-584 UAUGGUU chr5 na na
Hsa-Mir-190-P1_5p -2.24 0.22 4.84e-24 23 140 hsa-mir-190a MIR-190 GAUAUGU chr15 na na
Hsa-Mir-17-P2a_5p -2.22 0.23 1.80e-21 26 168 hsa-mir-18a MIR-17 AAGGUGC chr13 na na
Hsa-Mir-21_5p -2.10 0.14 5.68e-48 19240 105735 hsa-mir-21 MIR-21 AGCUUAU chr17 na na
Hsa-Mir-92-P1c_3p -1.90 0.21 3.08e-18 303 1398 hsa-mir-92b MIR-92 AUUGCAC chr1 na na
Hsa-Mir-10-P1a_5p -1.79 0.25 2.08e-11 25828 97123 hsa-mir-10a MIR-10 ACCCUGU chr17 na na
Hsa-Mir-146-P1_5p -1.77 0.29 4.42e-09 351 1453 hsa-mir-146a MIR-146 GAGAACU chr5 na na
Hsa-Mir-130-P2a_3p -1.75 0.17 1.02e-24 92 409 hsa-mir-301a MIR-130 AGUGCAA chr17 na na
Hsa-Mir-29-P2a_3p/P2b_3p -1.48 0.19 9.97e-14 107 380 hsa-mir-29b-1 MIR-29 AGCACCA chr7 na na
Hsa-Mir-17-P3c_5p -1.47 0.13 8.74e-29 100 370 hsa-mir-106b MIR-17 AAAGUGC chr7 na na
Hsa-Mir-17-P1c_5p -1.45 0.11 7.11e-37 647 2287 hsa-mir-93 MIR-17 AAAGUGC chr7 na na
Hsa-Mir-95-P2_3p -1.42 0.16 7.76e-18 40 142 hsa-mir-421 MIR-95 UCAACAG chrX na na
Hsa-Mir-17-P3a_5p -1.41 0.19 1.99e-13 437 1513 hsa-mir-20a MIR-17 AAAGUGC chr13 na na
Hsa-Mir-188-P2_5p -1.33 0.15 3.22e-17 279 914 hsa-mir-532 MIR-188 AUGCCUU chrX na na
Hsa-Let-7-P2c2_5p -1.26 0.14 4.74e-19 1738 5432 hsa-let-7i LET-7 GAGGUAG chr12 na na
Hsa-Let-7-P2b3_5p -1.22 0.15 7.97e-15 760 2149 hsa-mir-98 LET-7 GAGGUAG chrX na na
Hsa-Mir-210_3p -1.16 0.23 5.10e-06 106 291 hsa-mir-210 MIR-210 UGUGCGU chr11 na na
Hsa-Mir-425_5p -1.15 0.15 2.77e-13 247 700 hsa-mir-425 MIR-425 AUGACAC chr3 na na
Hsa-Mir-19-P1_3p -1.12 0.19 1.87e-08 134 379 hsa-mir-19a MIR-19 GUGCAAA chr13 na na
Hsa-Mir-132-P1_3p -1.06 0.17 6.29e-10 61 165 hsa-mir-132 MIR-132 AACAGUC chr17 na na
Hsa-Mir-130-P4a_3p -0.99 0.10 5.70e-21 70 177 hsa-mir-454 MIR-130 AGUGCAA chr17 na na
Hsa-Mir-652_3p -0.91 0.13 2.45e-11 44 106 hsa-mir-652 MIR-652 AUGGCGC chrX na na
Hsa-Mir-769_5p -0.81 0.13 9.49e-10 190 439 hsa-mir-769 MIR-769 GAGACCU chr19 na na
Hsa-Mir-362-P2_3p/P4_3p -0.69 0.17 7.46e-05 211 441 hsa-mir-500a MIR-362 UGCACCU chrX na na
Hsa-Mir-181-P2a_5p/P2b_5p -0.67 0.15 1.77e-05 611 1251 hsa-mir-181b-1 MIR-181 ACAUUCA chr1 na na
Hsa-Mir-17-P3c_3p -0.64 0.14 5.82e-06 105 212 hsa-mir-106b MIR-17 AAAGUGC chr7 na na
Hsa-Mir-19-P2a_3p/P2b_3p -0.60 0.18 1.90e-03 677 1297 hsa-mir-19b-1 MIR-19 GUGCAAA chr13 na na 
# Number of downregulated miRNA
signature_mirnas$number_downregulated
## [1] 54

pCRC vs nLu

column='tissue.type'
tissue_type_A <- 'normal.lung'
tissue_type_B <- 'tumor.colorect'
norm_adj_up       = "None"
norm_adj_down     = "None"
pCRC_adj_up   = "None"
pCRC_adj_down = "None"

coef <- paste(column, tissue_type_A, 'vs', tissue_type_B, sep='_')
res <- DeseqResult(dds, column, coef, tissue_type_A, tissue_type_B,
                   lfc.Threshold, rpm.Threshold,
                   norm_adj_up,
                   norm_adj_down)

dict_sig_mirna[paste(coef, "up",   sep='_')] <- list(res$up_mirna)
dict_sig_mirna[paste(coef, "down", sep='_')] <- list(res$down_mirna)
res_res <- res$res
res_dict[coef] <- res_res
plotMA(res$res, alpha=0.05)

# Plot volcano plot
VolcanoPlot(res$res, coef, res$sig,
            res$up_mirna, res$down_mirna,
            norm_adj_up, norm_adj_down,
            pCRC_adj_up, pCRC_adj_down)

ExpressionPlot(res$res, res$rpm, coef, res$sig,
               tissue_type_A, tissue_type_B,
               res$up_mirna, res$down_mirna,
               norm_adj_up, norm_adj_down,
               pCRC_adj_up, pCRC_adj_down)

signature_mirnas <- SigList(res, dds, tissue_type_A, tissue_type_B, coef,
                            norm_adj_up, norm_adj_down, 
                            pCRC_adj_up, pCRC_adj_down)
# Print list upregulated miRNA
signature_mirnas$up_mirna
Upregulated in tissue.type_normal.lung_vs_tumor.colorect
miRNA LFC lfcSE FDR RPM normal.lung RPM tumor.colorect miRBase_ID Family Seed Chr Cell-Type Specific Norm Background pCRC Background
Hsa-Mir-335_5p 1.10 0.19 1.98e-08 300 150 hsa-mir-335 MIR-335 CAAGAGC chr7 Retinal Epithelial Cell na na
Hsa-Mir-451_5p 3.02 0.43 1.09e-11 12496 1565 hsa-mir-451a MIR-451 AACCGUU chr17 Red Blood Cell na na
Hsa-Mir-144_5p 2.99 0.37 6.58e-15 451 57 hsa-mir-144 MIR-144 GAUAUCA chr17 Red Blood Cell na na
Hsa-Mir-145_5p 1.49 0.36 9.83e-05 2445 840 hsa-mir-145 MIR-145 UCCAGUU chr5 Mesenchymal na na
Hsa-Mir-24-P1_3p/P2_3p 0.73 0.22 2.23e-03 1084 626 hsa-mir-24-2 MIR-24 GGCUCAG chr19 Macrophage na na
Hsa-Mir-150_5p 1.73 0.39 1.07e-05 973 280 hsa-mir-150 MIR-150 CUCCCAA chr19 Lymphocyte na na
Hsa-Mir-15-P1a_5p 0.96 0.17 2.82e-08 854 453 hsa-mir-15a MIR-15 AGCAGCA chr13 CD14+ Monocyte na na
Hsa-Mir-486_5p 3.15 0.41 6.09e-14 22081 2013 hsa-mir-486-1 MIR-486 CCUGUAC chr8 c(“Platelet”, “Red Blood Cell”) na na
Hsa-Mir-126_5p 3.27 0.23 4.58e-44 33620 3455 hsa-mir-126 MIR-126 AUUAUUA chr9 c(“Endothelial Cell”, “Platelet”) na na
Hsa-Mir-146-P2_5p 1.39 0.37 3.72e-04 17435 6279 hsa-mir-146b MIR-146 GAGAACU chr10 c(“Dendritic Cell”, “Lymphocyte”) na na
Hsa-Mir-342_3p 2.20 0.28 2.02e-14 681 145 hsa-mir-342 MIR-342 CUCACAC chr14 c(“Dendritic Cell”, “Lymphocyte”, “Macrophage”) na na
Hsa-Mir-184_3p 4.90 0.90 1.36e-07 111 1 hsa-mir-184 MIR-184 GGACGGA chr15 na na
Hsa-Mir-34-P2a_5p 4.41 0.45 6.03e-22 116 5 hsa-mir-34b MIR-34 GGCAGUG chr11 na na
Hsa-Mir-34-P2b_5p 4.40 0.39 1.18e-28 748 33 hsa-mir-34c MIR-34 GGCAGUG chr11 na na
Hsa-Mir-30-P1a_5p 4.21 0.25 3.44e-60 60860 3165 hsa-mir-30a MIR-30 GUAAACA chr6 na na
Hsa-Mir-218-P1_5p/P2_5p 3.31 0.30 3.30e-27 374 37 hsa-mir-218-1 MIR-218 UGUGCUU chr4 na na
Hsa-Mir-15-P2c_5p 2.58 0.26 1.28e-21 1552 257 hsa-mir-195 MIR-15 AGCAGCA chr17 na na
Hsa-Mir-338-P1_3p 2.57 0.35 1.28e-12 605 98 hsa-mir-338 MIR-338 CCAGCAU chr17 na na
Hsa-Mir-10-P2c_5p 2.37 0.37 4.44e-10 804 142 hsa-mir-99a MIR-10 ACCCGUA chr21 na na
Hsa-Mir-10-P3b_5p 2.28 0.30 5.11e-13 8561 1649 hsa-mir-125a MIR-10 CCCUGAG chr19 na na
Hsa-Mir-130-P1a_3p 2.26 0.22 6.52e-23 1941 421 hsa-mir-130a MIR-130 AGUGCAA chr11 na na
Hsa-Let-7-P1c_5p 2.02 0.31 4.62e-10 2606 606 hsa-let-7c LET-7 GAGGUAG chr21 na na
Hsa-Mir-181-P1a_5p/P1b_5p 1.97 0.21 1.65e-19 80063 21233 hsa-mir-181a-1 MIR-181 ACAUUCA chr1 na na
Hsa-Mir-30-P1c_5p 1.87 0.18 4.93e-24 34088 9703 hsa-mir-30d MIR-30 GUAAACA chr8 na na
Hsa-Mir-101-P1_3p/P2_3p 1.85 0.20 1.47e-19 16084 4638 hsa-mir-101-1 MIR-101 UACAGUA chr1 na na
Hsa-Mir-26-P1_5p/P2_5p 1.82 0.15 1.88e-32 123947 35584 hsa-mir-26b MIR-26 UCAAGUA chr2 na na
Hsa-Mir-10-P3a_5p 1.72 0.35 2.27e-06 507 145 hsa-mir-125b-1 MIR-10 CCCUGAG chr11 na na
Hsa-Mir-181-P2a_5p/P2b_5p 1.68 0.20 4.86e-16 3799 1251 hsa-mir-181b-1 MIR-181 ACAUUCA chr1 na na
Hsa-Mir-10-P3c_5p 1.60 0.34 3.99e-06 822 239 hsa-mir-125b-2 MIR-10 CCCUGAG chr21 na na
Hsa-Mir-140_3p 1.56 0.17 1.01e-18 2195 784 hsa-mir-140 MIR-140 CCACAGG chr16 na na
Hsa-Mir-15-P1c_5p 1.56 0.25 2.16e-09 405 143 hsa-mir-497 MIR-15 AGCAGCA chr17 na na
Hsa-Mir-30-P2a_5p/P2b_5p/P2c_5p 1.54 0.17 1.46e-19 7603 2793 hsa-mir-30c-2 MIR-30 GUAAACA chr6 na na
Hsa-Mir-92-P2b_3p 1.53 0.36 8.68e-05 137 47 hsa-mir-363 MIR-92 AUUGCAC chrX na na
Hsa-Mir-15-P2a_5p/P2b_5p 1.48 0.19 3.24e-14 12836 4522 hsa-mir-16-1 MIR-15 AGCAGCA chr13 na na
Hsa-Mir-26-P3_5p 1.37 0.22 1.19e-09 9029 3467 hsa-mir-26a-1 MIR-26 UCAAGUA chr3 na na
Hsa-Mir-10-P2a_5p 1.32 0.36 6.59e-04 4056 1724 hsa-mir-100 MIR-10 ACCCGUA chr11 na na
Hsa-Let-7-P2b1_5p 1.23 0.14 9.88e-19 6253 2784 hsa-let-7f-1 LET-7 GAGGUAG chr9 na na
Hsa-Mir-331_3p 1.11 0.29 2.07e-04 128 60 hsa-mir-331 MIR-331 CCCCUGG chr12 na na
Hsa-Mir-10-P2b_5p 1.06 0.42 2.28e-02 5968 2594 hsa-mir-99b MIR-10 ACCCGUA chr19 na na
Hsa-Mir-148-P2_3p 0.98 0.34 6.10e-03 270 137 hsa-mir-152 MIR-148 CAGUGCA chr17 na na
Hsa-Mir-29-P1b_3p 0.96 0.23 6.29e-05 607 331 hsa-mir-29c MIR-29 AGCACCA chr1 na na
Hsa-Mir-652_3p 0.88 0.18 1.77e-06 187 106 hsa-mir-652 MIR-652 AUGGCGC chrX na na
Hsa-Mir-744_5p 0.88 0.24 5.22e-04 109 61 hsa-mir-744 MIR-744 GCGGGGC chr17 na na
Hsa-Let-7-P2b2_5p 0.84 0.17 3.85e-06 6575 3787 hsa-let-7b LET-7 GAGGUAG chr22 na na
Hsa-Mir-27-P1_3p/P2_3p 0.80 0.16 4.13e-06 50854 29444 hsa-mir-27a MIR-27 UCACAGU chr19 na na
Hsa-Mir-374-P2_5p 0.74 0.22 1.39e-03 148 95 hsa-mir-374b MIR-374 UAUAAUA chrX na na
Hsa-Mir-23-P1_3p/P2_3p 0.68 0.17 1.35e-04 5786 3766 hsa-mir-23a MIR-23 UCACAUU chr19 na na
Hsa-Mir-28-P2_3p 0.65 0.19 1.33e-03 6007 3830 hsa-mir-151a MIR-28 CGAGGAG chr8 na na 
# Number of upregulated miRNA
signature_mirnas$number_upregulated
## [1] 48
# Print list downregulated miRNA
signature_mirnas$down_mirna
Downregulated in tissue.type_normal.lung_vs_tumor.colorect
miRNA LFC lfcSE FDR RPM normal.lung RPM tumor.colorect miRBase_ID Family Seed Chr Cell-Type Specific Norm Background pCRC Background
Hsa-Mir-128-P1_3p/P2_3p -0.86 0.18 6.72e-06 116 233 hsa-mir-128-1 MIR-128 CACAGUG chr2 Neural na na
Hsa-Mir-192-P1_5p/P2_5p -6.50 0.36 4.31e-69 142 13717 hsa-mir-192 MIR-192 UGACCUA chr11 Epithelial Cell na na
Hsa-Mir-8-P2a_3p -3.88 0.26 9.46e-48 618 10330 hsa-mir-200b MIR-8 AAUACUG chr1 Epithelial Cell na na
Hsa-Mir-8-P2b_3p -1.75 0.26 7.14e-11 766 2727 hsa-mir-200c MIR-8 AAUACUG chr12 Epithelial Cell na na
Hsa-Mir-17-P1a_5p/P1b_5p -2.17 0.21 9.51e-23 184 900 hsa-mir-17 MIR-17 AAAGUGC chr13 CD14+ Monocyte na na
Hsa-Mir-7-P1_5p/P2_5p/P3_5p -5.51 0.42 5.13e-38 3 199 hsa-mir-7-1 MIR-7 GGAAGAC chr9 c(“Islet Cell”, “Neural”) na na
Hsa-Mir-375_3p -1.22 0.43 1.11e-02 2346 5263 hsa-mir-375 MIR-375 UUGUUCG chr2 c(“Epithelial Cell”, “Islet Cell”, “Neural”) na na
Hsa-Mir-196-P1_5p/P2_5p -6.60 0.38 7.65e-66 3 319 hsa-mir-196a-1 MIR-196 AGGUAGU chr17 na na
Hsa-Mir-194-P1_5p/P2_5p -6.59 0.31 7.31e-100 58 6167 hsa-mir-194-2 MIR-194 GUAACAG chr11 na na
Hsa-Mir-196-P3_5p -6.49 0.43 9.67e-50 7 828 hsa-mir-196b MIR-196 AGGUAGU chr7 na na
Hsa-Mir-192-P1_5p -6.17 0.31 1.45e-85 1684 132000 hsa-mir-192 MIR-192 UGACCUA chr11 na na
Hsa-Mir-577_5p -5.61 0.37 3.74e-49 3 142 hsa-mir-577 MIR-577 UAGAUAA chr4 na na
Hsa-Mir-31_5p -4.10 0.59 3.10e-12 24 627 hsa-mir-31 MIR-31 GGCAAGA chr9 na na
Hsa-Mir-8-P3a_3p -2.93 0.27 4.00e-27 262 2303 hsa-mir-429 MIR-8 AAUACUG chr1 na na
Hsa-Mir-17-P3a_5p -2.82 0.26 3.30e-27 194 1513 hsa-mir-20a MIR-17 AAAGUGC chr13 na na
Hsa-Mir-135-P3_5p -2.73 0.37 6.36e-13 20 155 hsa-mir-135b MIR-135 AUGGCUU chr1 na na
Hsa-Mir-127_3p -2.70 0.36 7.71e-13 327 2263 hsa-mir-127 MIR-127 CGGAUCC chr14 na na
Hsa-Mir-190-P1_5p -2.56 0.29 1.90e-17 22 140 hsa-mir-190a MIR-190 GAUAUGU chr15 na na
Hsa-Mir-224_5p -2.55 0.30 2.30e-16 59 395 hsa-mir-224 MIR-224 AAGUCAC chrX na na
Hsa-Mir-154-P36_3p -2.22 0.23 2.04e-21 39 195 hsa-mir-409 MIR-154 AAUGUUG chr14 na na
Hsa-Mir-8-P1a_3p -2.20 0.26 7.55e-16 353 1772 hsa-mir-200a MIR-8 AACACUG chr1 na na
Hsa-Mir-21_5p -2.20 0.19 1.45e-28 21349 105735 hsa-mir-21 MIR-21 AGCUUAU chr17 na na
Hsa-Mir-15-P1d_5p -2.15 0.34 9.48e-10 46 235 hsa-mir-424 MIR-15 AGCAGCA chrX na na
Hsa-Mir-17-P2a_5p -2.09 0.31 5.05e-11 33 168 hsa-mir-18a MIR-17 AAGGUGC chr13 na na
Hsa-Mir-130-P1b_3p -1.94 0.22 2.67e-18 28 116 hsa-mir-130b MIR-130 AGUGCAA chr22 na na
Hsa-Mir-96-P3_5p -1.94 0.37 3.95e-07 291 1063 hsa-mir-183 MIR-96 AUGGCAC chr7 na na
Hsa-Mir-96-P2_5p -1.91 0.27 1.11e-11 2685 10417 hsa-mir-182 MIR-96 UUGGCAA chr7 na na
Hsa-Mir-154-P23_3p -1.74 0.22 1.04e-13 41 148 hsa-mir-654 MIR-154 AUGUCUG chr14 na na
Hsa-Mir-19-P1_3p -1.65 0.26 1.09e-09 108 379 hsa-mir-19a MIR-19 GUGCAAA chr13 na na
Hsa-Mir-425_5p -1.65 0.21 2.02e-14 213 700 hsa-mir-425 MIR-425 AUGACAC chr3 na na
Hsa-Mir-154-P12_3p -1.63 0.24 1.28e-10 33 115 hsa-mir-410 MIR-154 AUAUAAC chr14 na na
Hsa-Mir-10-P1b_5p -1.55 0.41 4.05e-04 14125 39286 hsa-mir-10b MIR-10 ACCCUGU chr2 na na
Hsa-Mir-378_3p -1.45 0.22 9.02e-11 2189 6426 hsa-mir-378a MIR-378 CUGGACU chr5 na na
Hsa-Mir-584_5p -1.38 0.34 1.18e-04 39 110 hsa-mir-584 MIR-584 UAUGGUU chr5 na na
Hsa-Mir-92-P1a_3p/P1b_3p -1.27 0.25 8.94e-07 15850 39320 hsa-mir-92a-1 MIR-92 AUUGCAC chr13 na na
Hsa-Mir-17-P3c_5p -1.21 0.18 2.57e-11 143 370 hsa-mir-106b MIR-17 AAAGUGC chr7 na na
Hsa-Mir-17-P1c_5p -1.06 0.15 2.20e-11 1024 2287 hsa-mir-93 MIR-17 AAAGUGC chr7 na na
Hsa-Mir-203_3p -1.03 0.32 2.53e-03 1068 2307 hsa-mir-203a MIR-203 UGAAAUG chr14 na na
Hsa-Mir-210_3p -1.01 0.31 4.02e-03 138 291 hsa-mir-210 MIR-210 UGUGCGU chr11 na na
Hsa-Mir-95-P2_3p -0.99 0.22 1.35e-05 65 142 hsa-mir-421 MIR-95 UCAACAG chrX na na
Hsa-Mir-154-P13_5p -0.93 0.24 3.43e-04 161 328 hsa-mir-411 MIR-154 AGUAGAC chr14 na na
Hsa-Mir-193-P1b_3p -0.92 0.26 8.53e-04 72 145 hsa-mir-193b MIR-193 ACUGGCC chr16 na na
Hsa-Mir-19-P2a_3p/P2b_3p -0.91 0.24 5.22e-04 645 1297 hsa-mir-19b-1 MIR-19 GUGCAAA chr13 na na
Hsa-Mir-148-P1_3p -0.86 0.27 3.44e-03 18874 34704 hsa-mir-148a MIR-148 CAGUGCA chr7 na na
Hsa-Mir-146-P1_5p -0.85 0.39 4.76e-02 786 1453 hsa-mir-146a MIR-146 GAGAACU chr5 na na
Hsa-Mir-136_3p -0.81 0.24 1.54e-03 88 173 hsa-mir-136 MIR-136 AUCAUCG chr14 na na
Hsa-Mir-10-P1a_5p -0.79 0.34 4.36e-02 61989 97123 hsa-mir-10a MIR-10 ACCCUGU chr17 na na
Hsa-Mir-154-P9_3p -0.78 0.23 1.67e-03 135 253 hsa-mir-381 MIR-154 AUACAAG chr14 na na
Hsa-Mir-214_3p -0.74 0.26 8.05e-03 77 137 hsa-mir-214 MIR-214 CAGCAGG chr1 na na
Hsa-Mir-1307_3p -0.74 0.21 1.32e-03 80 139 hsa-mir-1307 MIR-1307 CGACCGG chr10 na na
Hsa-Let-7-P2b3_5p -0.72 0.21 1.18e-03 1289 2149 hsa-mir-98 LET-7 GAGGUAG chrX na na
Hsa-Mir-199-P1_3p/P2_3p/P3_3p -0.64 0.22 5.97e-03 3382 5772 hsa-mir-199b MIR-199 CAGUAGU chr9 na na 
# Number of downregulated miRNA
signature_mirnas$number_downregulated
## [1] 52
SubtractLFC <- function(x, y){
  z = x
  if ( is.na(x) | is.na(y) ){ return( z ) }
  else if (sign(x) == sign(y)){ z = x - y }
  if (sign(z) != sign(x)) { z = 0 }
  return( z )
}

SubtractAdjP <- function(x , y, xP, yP){
  z = xP
  if ( is.na(xP) | is.na(yP) ){ return( z ) }
  if ( sign(x) == sign(y) ){ 
    z = (xP + ( 1 - yP )) }
  if (z > 1) {z = 1}
  return(z)
}

pCRC vs mLi

#pCRC versus liver metastasis, control also with pCRC versus normal liver

column='tissue.type'
tissue_type_A <- 'metastasis.liver'
tissue_type_B <- 'tumor.colorect'
norm_adj_up       = dict_sig_mirna$tissue.type_normal.liver_vs_normal.colorect_up
norm_adj_down     = dict_sig_mirna$tissue.type_normal.liver_vs_normal.colorect_down
pCRC_adj_up   = dict_sig_mirna$tissue.type_normal.liver_vs_tumor.colorect_up
pCRC_adj_down = dict_sig_mirna$tissue.type_normal.liver_vs_tumor.colorect_down
palette <- 'jco'

coef <- paste(column, tissue_type_A, 'vs', tissue_type_B, sep='_')
res <- DeseqResult(dds, column, coef, tissue_type_A, tissue_type_B,
                   lfc.Threshold, rpm.Threshold,
                   norm_adj_up,
                   norm_adj_down,
                   pCRC_adj_up,
                   pCRC_adj_down)

dict_sig_mirna[paste(coef, "up",   sep='_')] <- list(res$up_mirna)
dict_sig_mirna[paste(coef, "down", sep='_')] <- list(res$down_mirna)
res_res <- res$res
res_dict[coef] <- res_res
plotMA(res$res, alpha=0.05)

# Plot volcano plot
VolcanoPlot(res$res, coef, res$sig,
            res$up_mirna, res$down_mirna,
            norm_adj_up, norm_adj_down,
            pCRC_adj_up, pCRC_adj_down)

ExpressionPlot(res$res, res$rpm, coef, res$sig,
               tissue_type_A, tissue_type_B,
               res$up_mirna, res$down_mirna,
               norm_adj_up, norm_adj_down,
               pCRC_adj_up, pCRC_adj_down)

signature_mirnas <- SigList(res, dds, tissue_type_A, tissue_type_B, coef,
                            norm_adj_up, norm_adj_down, 
                            pCRC_adj_up, pCRC_adj_down)
# Print list upregulated miRNA
signature_mirnas$up_mirna
Upregulated in tissue.type_metastasis.liver_vs_tumor.colorect
miRNA LFC lfcSE FDR RPM metastasis.liver RPM tumor.colorect miRBase_ID Family Seed Chr Cell-Type Specific Norm Background pCRC Background
Hsa-Mir-210_3p 1.26 0.18 4.73e-11 685 291 hsa-mir-210 MIR-210 UGUGCGU chr11
Hsa-Mir-592_5p 0.98 0.27 4.88e-03 137 75 hsa-mir-592 MIR-592 UGUGUCA chr7
Hsa-Mir-10-P1a_5p 0.86 0.19 8.04e-05 164925 97123 hsa-mir-10a MIR-10 ACCCUGU chr17
Hsa-Mir-1307_5p 0.85 0.17 2.99e-06 887 492 hsa-mir-1307 MIR-1307 CGACCGG chr10
Hsa-Mir-1247_5p 0.85 0.28 2.58e-02 127 69 hsa-mir-1247 MIR-1247 CCCGUCC chr14
Hsa-Mir-191_5p 0.74 0.15 3.67e-06 23253 13630 hsa-mir-191 MIR-191 AACGGAA chr3
Hsa-Mir-425_5p 0.73 0.12 2.90e-08 1117 700 hsa-mir-425 MIR-425 AUGACAC chr3
Hsa-Mir-8-P1b_3p 0.64 0.19 4.07e-03 11957 7518 hsa-mir-141 MIR-8 AACACUG chr12
Hsa-Mir-150_5p 1.08 0.22 2.99e-06 721 280 hsa-mir-150 MIR-150 CUCCCAA chr19 Lymphocyte yes
Hsa-Mir-342_3p 0.93 0.16 5.32e-08 320 145 hsa-mir-342 MIR-342 CUCACAC chr14 c(“Dendritic Cell”, “Lymphocyte”, “Macrophage”) yes
Hsa-Mir-331_3p 0.75 0.16 3.16e-05 107 60 hsa-mir-331 MIR-331 CCCCUGG chr12 yes
Hsa-Mir-15-P2a_5p/P2b_5p 0.68 0.11 1.34e-08 7588 4522 hsa-mir-16-1 MIR-15 AGCAGCA chr13 yes
Hsa-Mir-204-P1_5p 1.04 0.32 1.21e-06 260 62 hsa-mir-204 MIR-204 UCCCUUU chr9 Retinal Epithelial Cell yes yes
Hsa-Mir-335_5p 0.78 0.11 5.33e-11 261 150 hsa-mir-335 MIR-335 CAAGAGC chr7 Retinal Epithelial Cell yes yes
Hsa-Mir-122_5p 5.14 0.43 2.78e-29 3668 9 hsa-mir-122 MIR-122 GGAGUGU chr18 Hepatocyte yes yes
Hsa-Mir-10-P3c_5p 0.71 0.19 7.30e-04 480 239 hsa-mir-125b-2 MIR-10 CCCUGAG chr21 yes yes 
# Number of upregulated miRNA
signature_mirnas$number_upregulated
## [1] 16
# Print list downregulated miRNA
signature_mirnas$down_mirna
Downregulated in tissue.type_metastasis.liver_vs_tumor.colorect
miRNA LFC lfcSE FDR RPM metastasis.liver RPM tumor.colorect miRBase_ID Family Seed Chr Cell-Type Specific Norm Background pCRC Background
Hsa-Mir-486_5p -0.66 0.23 3.47e-02 1489 2013 hsa-mir-486-1 MIR-486 CCUGUAC chr8 c(“Platelet”, “Red Blood Cell”)
Hsa-Mir-31_5p -2.34 0.31 4.05e-13 88 627 hsa-mir-31 MIR-31 GGCAAGA chr9 yes
Hsa-Let-7-P2c2_5p -0.90 0.11 1.69e-14 2840 5432 hsa-let-7i LET-7 GAGGUAG chr12 yes
Hsa-Mir-143_3p -1.02 0.17 4.70e-08 72945 148431 hsa-mir-143 MIR-143 GAGAUGA chr5 Mesenchymal yes yes
Hsa-Mir-133-P1_3p/P2_3p/P3_3p -1.56 0.24 3.43e-10 38 116 hsa-mir-133a-2 MIR-133 UUGGUCC chr20 c(“Skeletal Myocyte”, “Stem Cell”) yes yes
Hsa-Mir-10-P1b_5p -1.62 0.23 1.76e-10 11785 39286 hsa-mir-10b MIR-10 ACCCUGU chr2 yes yes
Hsa-Mir-92-P1c_3p -0.68 0.17 2.83e-04 879 1398 hsa-mir-92b MIR-92 AUUGCAC chr1 yes yes
Hsa-Mir-146-P1_5p -0.63 0.22 1.91e-02 909 1453 hsa-mir-146a MIR-146 GAGAACU chr5 yes yes 
# Number of downregulated miRNA
signature_mirnas$number_downregulated
## [1] 8
res_tibble <- res$res
res_tibble$miRNA <- rownames(res_tibble)
res_tibble <- as_tibble(res_tibble)

metslfc <- res_dict$tissue.type_metastasis.liver_vs_tumor.colorect$log2FoldChange
normlfc <- res_dict$tissue.type_normal.liver_vs_normal.colorect$log2FoldChange

res_tibble$LFC_adj_background <- mapply(SubtractLFC, metslfc, normlfc)

metsP <- res_dict$tissue.type_metastasis.liver_vs_tumor.colorect$padj
normP <- res_dict$tissue.type_normal.liver_vs_normal.colorect$padj

res_tibble$padj_subt_normal <- mapply( SubtractAdjP, metslfc, normlfc, metsP, normP )

res_tibble %>% select(miRNA, log2FoldChange, lfcSE, LFC_adj_background, padj_subt_normal, baseMean, stat, pvalue, padj) %>% write_csv(path = '/Users/eirikhoy/Dropbox/projects/comet_analysis/data/Deseq_result_clm_vs_pcrc.csv')

pCRC vs mLu

#pCRC versus lung metastasis, control also with pCRC versus normal liver

column='tissue.type'
tissue_type_A <- 'metastasis.lung'
tissue_type_B <- 'tumor.colorect'
norm_adj_up       = dict_sig_mirna$tissue.type_normal.lung_vs_normal.colorect_up
norm_adj_down     = dict_sig_mirna$tissue.type_normal.lung_vs_normal.colorect_down
pCRC_adj_up   = dict_sig_mirna$tissue.type_normal.lung_vs_tumor.colorect_up
pCRC_adj_down = dict_sig_mirna$tissue.type_normal.lung_vs_tumor.colorect_down

coef <- paste(column, tissue_type_A, 'vs', tissue_type_B, sep='_')
res <- DeseqResult(dds, column, coef, tissue_type_A, tissue_type_B,
                   lfc.Threshold, rpm.Threshold,
                   norm_adj_up,
                   norm_adj_down,
                   pCRC_adj_up,
                   pCRC_adj_down)
dict_sig_mirna[paste(coef, "up",   sep='_')] <- list(res$up_mirna)
dict_sig_mirna[paste(coef, "down", sep='_')] <- list(res$down_mirna)
res_res <- res$res
res_dict[coef] <- res_res
plotMA(res$res, alpha=0.05)

# Plot volcano plot
VolcanoPlot(res$res, coef, res$sig,
            res$up_mirna, res$down_mirna,
            norm_adj_up, norm_adj_down,
            pCRC_adj_up, pCRC_adj_down)

ExpressionPlot(res$res, res$rpm, coef, res$sig,
               tissue_type_A, tissue_type_B,
               res$up_mirna, res$down_mirna,
               norm_adj_up, norm_adj_down,
               pCRC_adj_up, pCRC_adj_down)

signature_mirnas <- SigList(res, dds, tissue_type_A, tissue_type_B, coef,
                            norm_adj_up, norm_adj_down, 
                            pCRC_adj_up, pCRC_adj_down)
# Print list upregulated miRNA
signature_mirnas$up_mirna
Upregulated in tissue.type_metastasis.lung_vs_tumor.colorect
miRNA LFC lfcSE FDR RPM metastasis.lung RPM tumor.colorect miRBase_ID Family Seed Chr Cell-Type Specific Norm Background pCRC Background
Hsa-Mir-155_5p 0.76 0.18 7.24e-05 733 387 hsa-mir-155 MIR-155 UAAUGCU chr21 c(“Lymphocyte”, “Macrophage”)
Hsa-Mir-210_3p 1.15 0.19 1.55e-08 693 291 hsa-mir-210 MIR-210 UGUGCGU chr11
Hsa-Mir-142_5p 0.84 0.18 2.01e-05 7117 3453 hsa-mir-142 MIR-142 AUAAAGU chr17
Hsa-Mir-19-P2a_3p/P2b_3p 0.80 0.15 9.38e-07 2345 1297 hsa-mir-19b-1 MIR-19 GUGCAAA chr13
Hsa-Mir-8-P1b_3p 0.75 0.21 1.12e-03 15111 7518 hsa-mir-141 MIR-8 AACACUG chr12
Hsa-Mir-191_5p 0.74 0.16 9.81e-06 26989 13630 hsa-mir-191 MIR-191 AACGGAA chr3
Hsa-Mir-374-P1_5p 0.72 0.12 9.07e-09 217 115 hsa-mir-374a MIR-374 UAUAAUA chrX
Hsa-Mir-19-P1_3p 0.65 0.16 3.42e-04 597 379 hsa-mir-19a MIR-19 GUGCAAA chr13
Hsa-Mir-145_5p 0.67 0.22 6.74e-03 1629 840 hsa-mir-145 MIR-145 UCCAGUU chr5 Mesenchymal yes
Hsa-Mir-150_5p 1.14 0.24 2.51e-06 738 280 hsa-mir-150 MIR-150 CUCCCAA chr19 Lymphocyte yes
Hsa-Mir-10-P3c_5p 0.85 0.21 9.78e-05 574 239 hsa-mir-125b-2 MIR-10 CCCUGAG chr21 yes
Hsa-Mir-29-P1b_3p 0.71 0.14 3.39e-06 597 331 hsa-mir-29c MIR-29 AGCACCA chr1 yes
Hsa-Mir-15-P2a_5p/P2b_5p 0.71 0.12 1.51e-08 8258 4522 hsa-mir-16-1 MIR-15 AGCAGCA chr13 yes
Hsa-Mir-26-P3_5p 0.65 0.14 6.79e-06 6205 3467 hsa-mir-26a-1 MIR-26 UCAAGUA chr3 yes
Hsa-Mir-331_3p 0.62 0.18 1.05e-03 105 60 hsa-mir-331 MIR-331 CCCCUGG chr12 yes
Hsa-Mir-335_5p 0.88 0.12 1.45e-12 295 150 hsa-mir-335 MIR-335 CAAGAGC chr7 Retinal Epithelial Cell yes yes
Hsa-Mir-451_5p 0.87 0.26 1.88e-03 3152 1565 hsa-mir-451a MIR-451 AACCGUU chr17 Red Blood Cell yes yes
Hsa-Mir-24-P1_3p/P2_3p 0.74 0.14 7.89e-07 1289 626 hsa-mir-24-2 MIR-24 GGCUCAG chr19 Macrophage yes yes
Hsa-Mir-126_5p 1.67 0.15 6.56e-29 13328 3455 hsa-mir-126 MIR-126 AUUAUUA chr9 c(“Endothelial Cell”, “Platelet”) yes yes
Hsa-Mir-146-P2_5p 0.94 0.23 1.47e-04 15403 6279 hsa-mir-146b MIR-146 GAGAACU chr10 c(“Dendritic Cell”, “Lymphocyte”) yes yes
Hsa-Mir-342_3p 2.25 0.18 6.24e-36 872 145 hsa-mir-342 MIR-342 CUCACAC chr14 c(“Dendritic Cell”, “Lymphocyte”, “Macrophage”) yes yes
Hsa-Mir-34-P2b_5p 4.24 0.24 2.76e-67 989 33 hsa-mir-34c MIR-34 GGCAGUG chr11 yes yes
Hsa-Mir-34-P2a_5p 3.97 0.27 8.59e-45 128 5 hsa-mir-34b MIR-34 GGCAGUG chr11 yes yes
Hsa-Mir-30-P1a_5p 1.74 0.16 1.56e-26 13224 3165 hsa-mir-30a MIR-30 GUAAACA chr6 yes yes
Hsa-Mir-15-P2c_5p 1.64 0.17 6.99e-22 975 257 hsa-mir-195 MIR-15 AGCAGCA chr17 yes yes
Hsa-Mir-218-P1_5p/P2_5p 1.57 0.19 3.94e-16 135 37 hsa-mir-218-1 MIR-218 UGUGCUU chr4 yes yes
Hsa-Mir-374-P2_5p 1.14 0.14 1.78e-15 226 95 hsa-mir-374b MIR-374 UAUAAUA chrX yes yes
Hsa-Mir-338-P1_3p 1.10 0.22 1.51e-06 236 98 hsa-mir-338 MIR-338 CCAGCAU chr17 yes yes
Hsa-Mir-26-P1_5p/P2_5p 1.07 0.10 7.47e-28 84008 35584 hsa-mir-26b MIR-26 UCAAGUA chr2 yes yes
Hsa-Mir-10-P2c_5p 0.63 0.23 8.68e-03 289 142 hsa-mir-99a MIR-10 ACCCGUA chr21 yes yes
Hsa-Mir-130-P1a_3p 0.62 0.14 4.12e-05 729 421 hsa-mir-130a MIR-130 AGUGCAA chr11 yes yes 
# Number of upregulated miRNA
signature_mirnas$number_upregulated
## [1] 31
# Print list downregulated miRNA
signature_mirnas$down_mirna
Downregulated in tissue.type_metastasis.lung_vs_tumor.colorect
miRNA LFC lfcSE FDR RPM metastasis.lung RPM tumor.colorect miRBase_ID Family Seed Chr Cell-Type Specific Norm Background pCRC Background
Hsa-Mir-423_5p -1.17 0.17 1.35e-10 202 433 hsa-mir-423 MIR-423 GAGGGGC chr17
Hsa-Let-7-P1b_5p -0.86 0.16 2.55e-07 650 1057 hsa-let-7e LET-7 GAGGUAG chr19
Hsa-Mir-197_3p -0.74 0.13 1.76e-07 113 181 hsa-mir-197 MIR-197 UCACCAC chr1
Hsa-Mir-92-P1c_3p -0.72 0.18 2.22e-04 981 1398 hsa-mir-92b MIR-92 AUUGCAC chr1
Hsa-Mir-362-P2_3p/P4_3p -0.65 0.14 2.36e-05 302 441 hsa-mir-500a MIR-362 UGCACCU chrX
Hsa-Mir-221-P2_3p -0.60 0.14 8.50e-05 1101 1633 hsa-mir-222 MIR-221 GCUACAU chrX
Hsa-Mir-7-P1_5p/P2_5p/P3_5p -0.80 0.25 1.88e-03 106 199 hsa-mir-7-1 MIR-7 GGAAGAC chr9 c(“Islet Cell”, “Neural”) yes
Hsa-Mir-154-P36_3p -1.40 0.14 3.20e-21 80 195 hsa-mir-409 MIR-154 AAUGUUG chr14 yes
Hsa-Mir-154-P12_3p -1.38 0.15 7.64e-18 46 115 hsa-mir-410 MIR-154 AUAUAAC chr14 yes
Hsa-Mir-31_5p -1.01 0.34 1.99e-03 242 627 hsa-mir-31 MIR-31 GGCAAGA chr9 yes
Hsa-Mir-96-P2_5p -0.78 0.17 1.58e-05 6399 10417 hsa-mir-182 MIR-96 UUGGCAA chr7 yes
Hsa-Mir-199-P1_3p/P2_3p/P3_3p -0.65 0.14 1.06e-05 3904 5772 hsa-mir-199b MIR-199 CAGUAGU chr9 yes
Hsa-Mir-15-P1d_5p -0.63 0.21 5.51e-03 158 235 hsa-mir-424 MIR-15 AGCAGCA chrX yes
Hsa-Mir-92-P1a_3p/P1b_3p -0.63 0.15 1.76e-04 26467 39320 hsa-mir-92a-1 MIR-92 AUUGCAC chr13 yes
Hsa-Mir-143_3p -1.34 0.19 1.00e-11 67942 148431 hsa-mir-143 MIR-143 GAGAUGA chr5 Mesenchymal yes
Hsa-Mir-133-P1_3p/P2_3p/P3_3p -1.73 0.26 4.97e-11 37 116 hsa-mir-133a-2 MIR-133 UUGGUCC chr20 c(“Skeletal Myocyte”, “Stem Cell”) yes
Hsa-Mir-362-P3_3p -0.89 0.25 1.47e-03 61 108 hsa-mir-501 MIR-362 AUGCACC chrX yes
Hsa-Mir-8-P2a_3p -0.60 0.16 6.49e-04 6834 10330 hsa-mir-200b MIR-8 AAUACUG chr1 Epithelial Cell yes yes
Hsa-Mir-127_3p -1.99 0.22 3.22e-17 617 2263 hsa-mir-127 MIR-127 CGGAUCC chr14 yes yes
Hsa-Mir-154-P13_5p -1.34 0.15 3.38e-17 142 328 hsa-mir-411 MIR-154 AGUAGAC chr14 yes yes
Hsa-Mir-154-P9_3p -1.34 0.14 1.80e-18 107 253 hsa-mir-381 MIR-154 AUACAAG chr14 yes yes
Hsa-Mir-190-P1_5p -1.26 0.18 2.77e-11 58 140 hsa-mir-190a MIR-190 GAUAUGU chr15 yes yes
Hsa-Mir-154-P23_3p -1.22 0.14 1.46e-16 69 148 hsa-mir-654 MIR-154 AUGUCUG chr14 yes yes
Hsa-Mir-136_3p -1.09 0.15 5.02e-12 86 173 hsa-mir-136 MIR-136 AUCAUCG chr14 yes yes
Hsa-Mir-378_3p -0.69 0.14 2.25e-06 3998 6426 hsa-mir-378a MIR-378 CUGGACU chr5 yes yes
Hsa-Mir-10-P1b_5p -0.66 0.25 1.93e-02 30285 39286 hsa-mir-10b MIR-10 ACCCUGU chr2 yes yes 
# Number of downregulated miRNA
signature_mirnas$number_downregulated
## [1] 26
res_tibble <- res$res
res_tibble$miRNA <- rownames(res_tibble)
res_tibble <- as_tibble(res_tibble)

metslfc <- res_dict$tissue.type_metastasis.lung_vs_tumor.colorect$log2FoldChange
normlfc <- res_dict$tissue.type_normal.lung_vs_normal.colorect$log2FoldChange

res_tibble$LFC_adj_background <- mapply(SubtractLFC, metslfc, normlfc)

metsP <- res_dict$tissue.type_metastasis.lung_vs_tumor.colorect$padj
normP <- res_dict$tissue.type_normal.lung_vs_normal.colorect$padj

res_tibble$padj_subt_normal <- mapply(SubtractAdjP, metslfc, normlfc,metsP, normP)

res_tibble %>% select(miRNA, log2FoldChange, lfcSE, LFC_adj_background, padj_subt_normal, baseMean, stat, pvalue, padj) %>% write_csv(path = 
                      '/Users/eirikhoy/Dropbox/projects/comet_analysis/data/Deseq_result_mlu_vs_pcrc.csv')

pCRC vs PM

#pCRC versus PC metastasis, union of mLi and mLu normal control

column='tissue.type'
tissue_type_A <- 'metastasis.pc'
tissue_type_B <- 'tumor.colorect'
norm_adj_up       = union(dict_sig_mirna$tissue.type_normal.liver_vs_normal.colorect_up, dict_sig_mirna$tissue.type_normal.lung_vs_normal.colorect_up)
norm_adj_down     = union(dict_sig_mirna$tissue.type_normal.liver_vs_normal.colorect_down, dict_sig_mirna$tissue.type_normal.lung_vs_normal.colorect_down)
pCRC_adj_up       = union(dict_sig_mirna$tissue.type_normal.liver_vs_tumor.colorect_up, dict_sig_mirna$tissue.type_normal.lung_vs_tumor.colorect_up)
pCRC_adj_down     = union(dict_sig_mirna$tissue.type_normal.liver_vs_tumor.colorect_down, dict_sig_mirna$tissue.type_normal.lung_vs_tumor.colorect_down)

coef <- paste(column, tissue_type_A, 'vs', tissue_type_B, sep='_')
res <- DeseqResult(dds, column, coef, tissue_type_A, tissue_type_B,
                   lfc.Threshold, rpm.Threshold,
                   norm_adj_up,
                   norm_adj_down,
                   pCRC_adj_up,
                   pCRC_adj_down)

dict_sig_mirna[paste(coef, "up",   sep='_')] <- list(res$up_mirna)
dict_sig_mirna[paste(coef, "down", sep='_')] <- list(res$down_mirna)
res_res <- res$res
res_dict[coef] <- res_res
plotMA(res$res, alpha=0.05)

# Plot volcano plot
VolcanoPlot(res$res, coef, res$sig,
            res$up_mirna, res$down_mirna,
            norm_adj_up, norm_adj_down,
            pCRC_adj_up, pCRC_adj_down)

ExpressionPlot(res$res, res$rpm, coef, res$sig,
               tissue_type_A, tissue_type_B,
               res$up_mirna, res$down_mirna,
               norm_adj_up, norm_adj_down,
               pCRC_adj_up, pCRC_adj_down)

signature_mirnas <- SigList(res, dds, tissue_type_A, tissue_type_B, coef,
                            norm_adj_up, norm_adj_down, 
                            pCRC_adj_up, pCRC_adj_down)

# Print list upregulated miRNA
print("as no normal adjacent PC tissue was available, control was union of lung and liver normal adjacent")
## [1] "as no normal adjacent PC tissue was available, control was union of lung and liver normal adjacent"
signature_mirnas$up_mirna
Upregulated in tissue.type_metastasis.pc_vs_tumor.colorect
miRNA LFC lfcSE FDR RPM metastasis.pc RPM tumor.colorect miRBase_ID Family Seed Chr Cell-Type Specific Norm Background pCRC Background
Hsa-Mir-155_5p 0.70 0.17 1.59e-04 654 387 hsa-mir-155 MIR-155 UAAUGCU chr21 c(“Lymphocyte”, “Macrophage”)
Hsa-Mir-506-P4a1_3p/P4a2_3p/P4b_3p 2.72 0.29 5.75e-11 191 14 hsa-mir-509-1 MIR-506 GAUUGGU chrX
Hsa-Mir-506-P3_3p 2.69 0.29 6.35e-09 112 6 hsa-mir-508 MIR-506 GAUUGUA chrX
Hsa-Mir-154-P9_3p 0.85 0.14 1.43e-08 455 253 hsa-mir-381 MIR-154 AUACAAG chr14
Hsa-Mir-154-P36_3p 0.74 0.13 7.13e-07 330 195 hsa-mir-409 MIR-154 AAUGUUG chr14
Hsa-Mir-210_3p 0.71 0.18 3.37e-04 491 291 hsa-mir-210 MIR-210 UGUGCGU chr11
Hsa-Mir-1307_5p 0.62 0.17 8.29e-04 797 492 hsa-mir-1307 MIR-1307 CGACCGG chr10
Hsa-Mir-127_3p 0.61 0.20 1.42e-02 2888 2263 hsa-mir-127 MIR-127 CGGAUCC chr14
Hsa-Mir-191_5p 0.61 0.15 2.41e-04 20429 13630 hsa-mir-191 MIR-191 AACGGAA chr3
Hsa-Mir-150_5p 0.70 0.21 2.45e-03 528 280 hsa-mir-150 MIR-150 CUCCCAA chr19 Lymphocyte yes
Hsa-Mir-154-P13_5p 0.94 0.14 2.08e-09 628 328 hsa-mir-411 MIR-154 AGUAGAC chr14 yes
Hsa-Mir-15-P2a_5p/P2b_5p 0.80 0.11 7.44e-11 8204 4522 hsa-mir-16-1 MIR-15 AGCAGCA chr13 yes
Hsa-Mir-136_3p 0.69 0.14 1.18e-05 274 173 hsa-mir-136 MIR-136 AUCAUCG chr14 yes
Hsa-Mir-335_5p 0.59 0.11 2.33e-06 232 150 hsa-mir-335 MIR-335 CAAGAGC chr7 Retinal Epithelial Cell yes yes
Hsa-Mir-122_5p 1.45 0.29 9.08e-08 244 9 hsa-mir-122 MIR-122 GGAGUGU chr18 Hepatocyte yes yes
Hsa-Mir-486_5p 0.76 0.22 2.47e-03 4442 2013 hsa-mir-486-1 MIR-486 CCUGUAC chr8 c(“Platelet”, “Red Blood Cell”) yes yes
Hsa-Mir-126_5p 0.63 0.14 2.84e-05 5738 3455 hsa-mir-126 MIR-126 AUUAUUA chr9 c(“Endothelial Cell”, “Platelet”) yes yes
Hsa-Mir-342_3p 0.76 0.16 1.39e-05 275 145 hsa-mir-342 MIR-342 CUCACAC chr14 c(“Dendritic Cell”, “Lymphocyte”, “Macrophage”) yes yes
Hsa-Mir-10-P2c_5p 1.10 0.20 1.07e-06 349 142 hsa-mir-99a MIR-10 ACCCGUA chr21 yes yes
Hsa-Let-7-P1c_5p 0.95 0.18 1.40e-06 1282 606 hsa-let-7c LET-7 GAGGUAG chr21 yes yes
Hsa-Mir-10-P3c_5p 0.80 0.19 1.02e-04 497 239 hsa-mir-125b-2 MIR-10 CCCUGAG chr21 yes yes
Hsa-Mir-154-P23_3p 0.80 0.13 5.21e-08 256 148 hsa-mir-654 MIR-154 AUGUCUG chr14 yes yes
Hsa-Mir-130-P1a_3p 0.70 0.13 1.40e-06 728 421 hsa-mir-130a MIR-130 AGUGCAA chr11 yes yes
Hsa-Mir-15-P2c_5p 0.60 0.15 3.85e-04 413 257 hsa-mir-195 MIR-15 AGCAGCA chr17 yes yes
Hsa-Mir-26-P1_5p/P2_5p 0.59 0.09 3.22e-09 52635 35584 hsa-mir-26b MIR-26 UCAAGUA chr2 yes yes 
# Number of upregulated miRNA
signature_mirnas$number_upregulated
## [1] 25
# Print list downregulated miRNA
print("as no normal adjacent PC tissue was available, control was union of lung and liver normal adjacent")
## [1] "as no normal adjacent PC tissue was available, control was union of lung and liver normal adjacent"
signature_mirnas$down_mirna
Downregulated in tissue.type_metastasis.pc_vs_tumor.colorect
miRNA LFC lfcSE FDR RPM metastasis.pc RPM tumor.colorect miRBase_ID Family Seed Chr Cell-Type Specific Norm Background pCRC Background
Hsa-Mir-223_3p -0.94 0.21 4.62e-05 280 619 hsa-mir-223 MIR-223 GUCAGUU chrX c(“Dendritic Cell”, “Macrophage”)
Hsa-Mir-143_3p -0.86 0.17 6.37e-06 77753 148431 hsa-mir-143 MIR-143 GAGAUGA chr5 Mesenchymal yes yes
Hsa-Mir-133-P1_3p/P2_3p/P3_3p -1.82 0.23 3.20e-14 25 116 hsa-mir-133a-2 MIR-133 UUGGUCC chr20 c(“Skeletal Myocyte”, “Stem Cell”) yes yes 
# Number of downregulated miRNA
signature_mirnas$number_downregulated
## [1] 3
res_tibble <- res$res
res_tibble$miRNA <- rownames(res_tibble)
res_tibble <- as_tibble(res_tibble)

metslfc <- res_dict$tissue.type_metastasis.pc_vs_tumor.colorect$log2FoldChange
normlfc <- ( (res_dict$tissue.type_normal.lung_vs_normal.colorect$log2FoldChange + res_dict$tissue.type_normal.liver_vs_normal.colorect$log2FoldChange) / 2)

res_tibble$LFC_adj_background <- mapply(SubtractLFC, metslfc, normlfc)

metsP <- res_dict$tissue.type_metastasis.pc_vs_tumor.colorect$padj
normP <- ( (res_dict$tissue.type_normal.lung_vs_normal.colorect$padj - res_dict$tissue.type_normal.liver_vs_normal.colorect$padj) / 2 )

res_tibble$padj_subt_normal <- mapply(SubtractAdjP, metslfc, normlfc,metsP, normP)


res_tibble %>% select(miRNA, log2FoldChange, lfcSE, LFC_adj_background, padj_subt_normal, baseMean, stat, pvalue, padj) %>% write_csv(path = '/Users/eirikhoy/Dropbox/projects/comet_analysis/data/Deseq_result_pc_vs_pcrc.csv')

All mCRC vs pCRC

DeseqObject <- function(DESIGN, countdata, coldata, consensus="None", sample_type="None", Ref) {
  "
  Function to create DESeq2 object
  "
  
  dds <- DESeqDataSetFromMatrix(countData = countdata,
                                colData = coldata,
                                design = as.formula(paste("~", DESIGN)))
    # Kick out non-consensus samples
  if (!(consensus == "None")) {
    dds <- dds[, dds$paper %in% consensus]
  }

  # Kick out samples that are not bulk tissue
  if (!(sample_type == "None")) {
    dds <- dds[, dds$sample_type == sample_type]
  }

  dds$type <- relevel(dds$type, ref=ref)
  dds$type <- droplevels(dds$type)
  
  dds <- DESeq(dds,
               parallel=TRUE,
               BPPARAM=MulticoreParam(3)
               )

  return(dds)
}
SigList <- function(res, dds, tissue_type_A, tissue_type_B, coef,
                            norm_adj_up, norm_adj_down, 
                            pCRC_adj_up, pCRC_adj_down){
  "
  Function to create annotated lists of signature miRNA
  Return will print upregulated or downregulated miRNA, 
  by printing <signature_list>$up_mirna
  or          <signature_list>$down_mirna
  "
  group_A_rpm <- rowMeans(res$rpm[res$sig, dds$type == tissue_type_A])
  group_A_rpm_std <- rowSds(res$rpm[res$sig, dds$type == tissue_type_A])
  group_B_rpm <- rowMeans(res$rpm[res$sig, dds$type == tissue_type_B])
  group_B_rpm_std <- rowSds(res$rpm[res$sig, dds$type == tissue_type_B])
  lfc.deseq2  <- res$res[res$sig, ]$log2FoldChange
  lfcSE.deseq2<- res$res[res$sig, ]$lfcSE
  neg.log.10.adj.p <- -log10(res$res[res$sig, ]$padj)
  signature_mirna <- res$sig
  sig_list <- dplyr::tibble(signature_mirna, lfc.deseq2, lfcSE.deseq2,
                            group_A_rpm, #group_A_rpm_std,
                            group_B_rpm, #group_B_rpm_std,
                            neg.log.10.adj.p)
  
  # create list of upregulated mirna
  up_mirna <- sig_list %>%
    filter(lfc.deseq2 > lfc.Threshold) %>% 
    
    # Annotate which miRNA are cell markers
    mutate(
      cell_marker = ifelse(signature_mirna %in% names(cell_spec_dict_inv), cell_spec_dict_inv[signature_mirna], '')) %>%
    mutate(
      cell_marker = cell_spec(cell_marker, color = ifelse(cell_marker != '', 'white', 'black'),
                              background = ifelse(cell_marker != '', 'blue', 'white'),
                              bold = ifelse(cell_marker != '', F, F))) %>%
  # Annotate which miRNA are present in blood cells
    mutate(
      blood_cell = ifelse(signature_mirna %in% blood.cell.mirna, 'yes', '')) %>%
    mutate(
      blood_cell = cell_spec(blood_cell, color = ifelse(blood_cell == 'yes', 'white', 'black'),
                              background = ifelse(blood_cell == 'yes', 'red', 'white'),
                              bold = ifelse(blood_cell == 'yes', T, F)))

    # Annotate which miRNA are in normal_adjacent
    if (norm_adj_up != "None") {
      up_mirna <- up_mirna %>%
        mutate(
          norm_adj = ifelse(signature_mirna %in% norm_adj_up, 'yes', '')) %>%
        mutate(
          norm_adj = cell_spec(norm_adj, color = ifelse(norm_adj == 'yes', 'white', 'black'),
                               background = ifelse(norm_adj == 'yes', 'black', 'white'),
                               bold = ifelse(norm_adj == 'yes', T, F))
        )
    }
    else up_mirna$norm_adj <- "na"
    
  # Annotate which miRNA are in pCRC_adjacent
    if (pCRC_adj_up != "None") {
      up_mirna <- up_mirna %>%
        mutate(
          pCRC_adj = ifelse(signature_mirna %in% pCRC_adj_up, 'yes', '')) %>%
        mutate(
          pCRC_adj = cell_spec(pCRC_adj, color = ifelse(pCRC_adj == 'yes', 'white', 'black'),
                               background = ifelse(pCRC_adj == 'yes', 'black', 'white'),
                               bold = ifelse(pCRC_adj == 'yes', T, F))
        )
    }
    else up_mirna$pCRC_adj <- "na"

    # number of upregulated miRNA
    number_upregulated <- dim(up_mirna)[1]

  # Create kable list with annotations
    up_mirna <- up_mirna %>%
      arrange(-lfc.deseq2) %>%
      arrange(desc(cell_marker)) %>%
      arrange(pCRC_adj) %>%
      arrange(norm_adj) %>%
      kable(col.names = c("miRNA", "LFC", "lfcSE",
                          paste('RPM', tissue_type_A), #paste('std', tissue_type_A), 
                          paste('RPM', tissue_type_B), #paste('std', tissue_type_B), 
                          "-log10(adj p-value)", "cell_marker", "blood_cell", 'norm_adj', 'pCRC_adj'),
            escape = F, booktabs = F, caption = paste("Upregulated in ", coef),
            digits = c(0, 2, 2, 0, 0, 3, 2, 3, 0, 0, 0, 0)) %>%
      kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = T, 
                  fixed_thead = list(enabled = T)) %>%
      column_spec(2, bold = T)
    
  # create list of downregulated miRNA
  down_mirna <- sig_list %>%
    filter(lfc.deseq2 < -lfc.Threshold) %>% 
    
    # Annotate which miRNA are cell markers
    mutate(
      cell_marker = ifelse(signature_mirna %in% names(cell_spec_dict_inv), cell_spec_dict_inv[signature_mirna], '')) %>%
    mutate(
      cell_marker = cell_spec(cell_marker, color = ifelse(cell_marker != '', 'white', 'black'),
                              background = ifelse(cell_marker != '', 'blue', 'white'),
                              bold = ifelse(cell_marker != '', F, F))) %>%
  # Annotate which miRNA are present in blood cells
    mutate(
      blood_cell = ifelse(signature_mirna %in% blood.cell.mirna, 'yes', '')) %>%
    mutate(
      blood_cell = cell_spec(blood_cell, color = ifelse(blood_cell == 'yes', 'white', 'black'),
                              background = ifelse(blood_cell == 'yes', 'red', 'white'),
                              bold = ifelse(blood_cell == 'yes', T, F)))

    # Annotate which miRNA are in normal_adjacent
    if (norm_adj_down != "None") {
      down_mirna <- down_mirna %>%
        mutate(
          norm_adj = ifelse(signature_mirna %in% norm_adj_down, 'yes', '')) %>%
        mutate(
          norm_adj = cell_spec(norm_adj, color = ifelse(norm_adj == 'yes', 'white', 'black'),
                               background = ifelse(norm_adj == 'yes', 'black', 'white'),
                               bold = ifelse(norm_adj == 'yes', T, F))
        )
    }
    else down_mirna$norm_adj <- "na"

    # Annotate which miRNA are in pCRC_adjacent
    if (pCRC_adj_down != "None") {
      down_mirna <- down_mirna %>%
        mutate(
          pCRC_adj = ifelse(signature_mirna %in% pCRC_adj_down, 'yes', '')) %>%
        mutate(
          pCRC_adj = cell_spec(pCRC_adj, color = ifelse(pCRC_adj == 'yes', 'white', 'black'),
                               background = ifelse(pCRC_adj == 'yes', 'black', 'white'),
                               bold = ifelse(pCRC_adj == 'yes', T, F))
        )
    }
    else down_mirna$pCRC_adj <- "na"
  
    # number of upregulated miRNA
    number_downregulated <- dim(down_mirna)[1]

  # Create kable list with annotations    
    down_mirna <- down_mirna %>%
      arrange(lfc.deseq2) %>%
      arrange(desc(cell_marker)) %>%
      arrange(pCRC_adj) %>%
      arrange(norm_adj) %>%
      kable(col.names = c("miRNA", "LFC", "lfcSE",
                          paste('RPM', tissue_type_A), #paste('std', tissue_type_A), 
                          paste('RPM', tissue_type_B), #paste('std', tissue_type_B), 
                          "-log10(adj p-value)", "cell_marker", "blood_cell", 'norm_adj', 'pCRC_adj'),
            escape = F, booktabs = F, caption = paste("Downregulated in ", coef),
            digits = c(0, 2, 2, 0, 0, 3, 2, 3, 0, 0, 0, 0)) %>%
      kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = T, 
                  fixed_thead = list(enabled = T)) %>%
      column_spec(2, bold = T)
    

  # Function return is to print kable, either upregulated or downregulated miRNA
  return_list = list("up_mirna" = up_mirna, "down_mirna" = down_mirna, 
                     "number_upregulated" = number_upregulated, 
                     "number_downregulated" = number_downregulated)
  return(return_list)
}
design <- as.formula(~ type)
ref <- 'tumor'
dds <- DeseqObject(design, countdata, sampleinfo, "None", "None", ref)
#
column='type'
tissue_type_A <- 'metastasis'
tissue_type_B <- 'tumor'
norm_adj_up       = union(dict_sig_mirna$tissue.type_normal.liver_vs_normal.colorect_up, dict_sig_mirna$tissue.type_normal.lung_vs_normal.colorect_up)
norm_adj_down     = union(dict_sig_mirna$tissue.type_normal.liver_vs_normal.colorect_down, dict_sig_mirna$tissue.type_normal.lung_vs_normal.colorect_down)
pCRC_adj_up       = union(dict_sig_mirna$tissue.type_normal.liver_vs_tumor.colorect_up, dict_sig_mirna$tissue.type_normal.lung_vs_tumor.colorect_up)
pCRC_adj_down     = union(dict_sig_mirna$tissue.type_normal.liver_vs_tumor.colorect_down, dict_sig_mirna$tissue.type_normal.lung_vs_tumor.colorect_down)

coef <- paste(column, tissue_type_A, 'vs', tissue_type_B, sep='_')
res <- DeseqResult(dds, column, coef, tissue_type_A, tissue_type_B,
                   lfc.Threshold, rpm.Threshold,
                   norm_adj_up,
                   norm_adj_down,
                   pCRC_adj_up,
                   pCRC_adj_down)
## using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
## 
## Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
## See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
## Reference: https://doi.org/10.1093/bioinformatics/bty895
## Warning in if (!(norm_adj_up == "None")) {: the condition has length > 1 and
## only the first element will be used
## Warning in if (!(norm_adj_down == "None")) {: the condition has length > 1 and
## only the first element will be used
## Warning in if (!(pCRC_adj_up == "None")) {: the condition has length > 1 and
## only the first element will be used
## Warning in if (!(pCRC_adj_down == "None")) {: the condition has length > 1 and
## only the first element will be used
dict_sig_mirna[paste(coef, "up",   sep='_')] <- list(res$up_mirna)
dict_sig_mirna[paste(coef, "down", sep='_')] <- list(res$down_mirna)
res_res <- res$res
res_dict[coef] <- res_res
## Warning in `[<-`(`*tmp*`, coef, value = new("DESeqResults", priorInfo = list(:
## implicit list embedding of S4 objects is deprecated
plotMA(res$res, alpha=0.05)

# Plot volcano plot
VolcanoPlot(res$res, coef, res$sig,
            res$up_mirna, res$down_mirna,
            norm_adj_up, norm_adj_down,
            pCRC_adj_up, pCRC_adj_down)
## Warning in if (norm_adj_up != "None" & length(up_mirna > 0)) {: the condition
## has length > 1 and only the first element will be used
## Warning in if (norm_adj_down != "None" & length(down_mirna > 0)) {: the
## condition has length > 1 and only the first element will be used
## Warning in if (pCRC_adj_up != "None" & length(up_mirna > 0)) {: the condition
## has length > 1 and only the first element will be used
## Warning in if (pCRC_adj_down != "None" & length(down_mirna > 0)) {: the
## condition has length > 1 and only the first element will be used

#ExpressionPlot(res$res, res$rpm, coef, res$sig,
#               tissue_type_A, tissue_type_B,
#               res$up_mirna, res$down_mirna,
#               norm_adj_up, norm_adj_down,
#               pCRC_adj_up, pCRC_adj_down)

signature_mirnas <- SigList(res, dds, tissue_type_A, tissue_type_B, coef,
                            norm_adj_up, norm_adj_down, 
                            pCRC_adj_up, pCRC_adj_down)
## Warning in if (norm_adj_up != "None") {: the condition has length > 1 and only
## the first element will be used
## Warning in if (pCRC_adj_up != "None") {: the condition has length > 1 and only
## the first element will be used
## Warning in if (norm_adj_down != "None") {: the condition has length > 1 and only
## the first element will be used
## Warning in if (pCRC_adj_down != "None") {: the condition has length > 1 and only
## the first element will be used
# Print list upregulated miRNA
print("control was union of lung and liver normal adjacent")
## [1] "control was union of lung and liver normal adjacent"
signature_mirnas$up_mirna
Upregulated in type_metastasis_vs_tumor
miRNA LFC lfcSE RPM metastasis RPM tumor -log10(adj p-value) cell_marker blood_cell norm_adj pCRC_adj
Hsa-Mir-210_3p 1.10 0.13 625 291 14.551
Hsa-Mir-1307_5p 0.72 0.12 837 492 7.519
Hsa-Mir-191_5p 0.72 0.11 23467 13630 9.458
Hsa-Mir-8-P1b_3p 0.66 0.17 12715 7518 3.068
Hsa-Mir-10-P1a_5p 0.59 0.15 140769 97123 3.469
Hsa-Mir-150_5p 1.05 0.16 664 280 9.458 Lymphocyte yes
Hsa-Mir-15-P2a_5p/P2b_5p 0.74 0.08 7988 4522 18.164 yes
Hsa-Mir-331_3p 0.70 0.12 104 60 7.519 yes
Hsa-Mir-335_5p 0.76 0.09 262 150 15.670 Retinal Epithelial Cell yes yes
Hsa-Mir-122_5p 1.56 0.31 1459 9 24.422 Hepatocyte yes yes
Hsa-Mir-126_5p 0.92 0.12 7434 3455 12.397 c(“Endothelial Cell”, “Platelet”) yes yes
Hsa-Mir-342_3p 1.46 0.13 472 145 27.005 c(“Dendritic Cell”, “Lymphocyte”, “Macrophage”) yes yes
Hsa-Mir-34-P2b_5p 2.61 0.21 332 33 19.564 yes yes
Hsa-Mir-30-P1a_5p 0.93 0.13 7100 3165 10.616 yes yes
Hsa-Mir-15-P2c_5p 0.87 0.12 537 257 10.661 yes yes
Hsa-Mir-10-P3c_5p 0.82 0.15 514 239 6.775 yes yes
Hsa-Mir-26-P1_5p/P2_5p 0.71 0.07 60843 35584 23.244 yes yes
Hsa-Mir-338-P1_3p 0.70 0.16 169 98 4.263 yes yes
Hsa-Mir-10-P2c_5p 0.70 0.17 264 142 3.839 yes yes 
# Number of upregulated miRNA
signature_mirnas$number_upregulated
## [1] 19
# Print list downregulated miRNA
print("control was union of lung and liver normal adjacent")
## [1] "control was union of lung and liver normal adjacent"
signature_mirnas$down_mirna
Downregulated in type_metastasis_vs_tumor
miRNA LFC lfcSE RPM metastasis RPM tumor -log10(adj p-value) cell_marker blood_cell norm_adj pCRC_adj
Hsa-Mir-7-P1_5p/P2_5p/P3_5p -0.60 0.18 112 199 2.657 c(“Islet Cell”, “Neural”) yes
Hsa-Mir-31_5p -0.68 0.23 339 627 2.235 yes
Hsa-Mir-143_3p -1.08 0.14 72990 148431 13.050 Mesenchymal yes yes
Hsa-Mir-133-P1_3p/P2_3p/P3_3p -1.80 0.19 33 116 18.097 c(“Skeletal Myocyte”, “Stem Cell”) yes yes 
# Number of downregulated miRNA
signature_mirnas$number_downregulated
## [1] 4
res_dict
## $tissue.type_normal.liver_vs_normal.colorect
## log2 fold change (MAP): tissue.type normal.liver vs normal.colorect 
## Wald test p-value: tissue.type normal.liver vs normal.colorect 
## DataFrame with 389 rows and 6 columns
##                                          baseMean     log2FoldChange
##                                         <numeric>          <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 89174.7542403281 -0.114702438165718
## Hsa-Let-7-P1b_5p                 3359.93816553835 -0.320114053507846
## Hsa-Let-7-P1c_5p                 3992.69006418194   3.18371450404926
## Hsa-Let-7-P2a3_5p                52000.6252125236  0.135005279299899
## Hsa-Let-7-P2b1_5p                9751.60360980849  0.321769922951614
## ...                                           ...                ...
## Hsa-Mir-95-P2_3p                 390.216333625945 -0.259423173236495
## Hsa-Mir-95-P3_5p                 3.95328293251241  0.578777309350614
## Hsa-Mir-96-P1_5p                 202.656949570096  -2.86706387684201
## Hsa-Mir-96-P2_5p                 27209.4573430933  -2.64828835135873
## Hsa-Mir-96-P3_5p                 3344.18853507094  -3.56720648489265
##                                              lfcSE               stat
##                                          <numeric>          <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.137100418774597 -0.876702398466493
## Hsa-Let-7-P1b_5p                 0.223907107455248   -1.4689025908534
## Hsa-Let-7-P1c_5p                 0.279081137786442   11.1516794079232
## Hsa-Let-7-P2a3_5p                0.120493686572101   1.11711255877344
## Hsa-Let-7-P2b1_5p                0.122630483027464   2.60055162322238
## ...                                            ...                ...
## Hsa-Mir-95-P2_3p                  0.19641408635223  -1.08552839444029
## Hsa-Mir-95-P3_5p                 0.384387609595463   1.62826495421394
## Hsa-Mir-96-P1_5p                 0.333638316705081  -7.80833454343775
## Hsa-Mir-96-P2_5p                 0.243241112732279  -10.2148298811257
## Hsa-Mir-96-P3_5p                 0.324276388539775  -10.0394868651728
##                                                pvalue                 padj
##                                             <numeric>            <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p    0.380648303687109    0.543582632940632
## Hsa-Let-7-P1b_5p                    0.141859211826177    0.240787346389169
## Hsa-Let-7-P1c_5p                 7.02674679240312e-29 1.59961824038824e-27
## Hsa-Let-7-P2a3_5p                   0.263946201391012    0.404220147427516
## Hsa-Let-7-P2b1_5p                  0.0093074014267008   0.0220979408106332
## ...                                               ...                  ...
## Hsa-Mir-95-P2_3p                    0.277687694717835    0.421431913160009
## Hsa-Mir-95-P3_5p                    0.103468717163438     0.18284197964498
## Hsa-Mir-96-P1_5p                 5.79486025768406e-15 5.33954980886603e-14
## Hsa-Mir-96-P2_5p                  1.7017739490561e-24 2.99357508311232e-23
## Hsa-Mir-96-P3_5p                 1.02204432533755e-23 1.71970066915492e-22
## 
## $tissue.type_normal.lung_vs_normal.colorect
## log2 fold change (MAP): tissue.type normal.lung vs normal.colorect 
## Wald test p-value: tissue.type normal.lung vs normal.colorect 
## DataFrame with 389 rows and 6 columns
##                                          baseMean      log2FoldChange
##                                         <numeric>           <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 89174.7542403281   0.159488915677012
## Hsa-Let-7-P1b_5p                 3359.93816553835   0.197555867999725
## Hsa-Let-7-P1c_5p                 3992.69006418194    1.96610542233694
## Hsa-Let-7-P2a3_5p                52000.6252125236   0.289175178805092
## Hsa-Let-7-P2b1_5p                9751.60360980849    1.10414120876771
## ...                                           ...                 ...
## Hsa-Mir-95-P2_3p                 390.216333625945   0.165907582077109
## Hsa-Mir-95-P3_5p                 3.95328293251241 -0.0273303033157357
## Hsa-Mir-96-P1_5p                 202.656949570096  -0.415869888949985
## Hsa-Mir-96-P2_5p                 27209.4573430933  -0.107488420248239
## Hsa-Mir-96-P3_5p                 3344.18853507094  -0.456955953013122
##                                              lfcSE                stat
##                                          <numeric>           <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.171252727891764   0.886346895351987
## Hsa-Let-7-P1b_5p                 0.280263432235887   0.631339921419183
## Hsa-Let-7-P1c_5p                 0.350196583213482    5.61986491346816
## Hsa-Let-7-P2a3_5p                 0.15044933221432    1.91622249772382
## Hsa-Let-7-P2b1_5p                0.153065517284977    7.17242652538056
## ...                                            ...                 ...
## Hsa-Mir-95-P2_3p                 0.243046643416424   0.843104626057063
## Hsa-Mir-95-P3_5p                 0.458035265321474   0.143432621264712
## Hsa-Mir-96-P1_5p                 0.388545838443054  -0.690134766070179
## Hsa-Mir-96-P2_5p                 0.304517199045329 -0.0704700708063357
## Hsa-Mir-96-P3_5p                 0.404724536391786  -0.772763885607241
##                                                pvalue                 padj
##                                             <numeric>            <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p    0.375430626009251    0.530261504618906
## Hsa-Let-7-P1b_5p                     0.52781828944235    0.684422033196025
## Hsa-Let-7-P1c_5p                 1.91106824923059e-08 1.57358172862179e-07
## Hsa-Let-7-P2a3_5p                  0.0553367808976523    0.104465044914105
## Hsa-Let-7-P2b1_5p                7.36799615073784e-13 1.05607944827242e-11
## ...                                               ...                  ...
## Hsa-Mir-95-P2_3p                    0.399169931746439    0.555679005704575
## Hsa-Mir-95-P3_5p                    0.885948521274591    0.960397976843884
## Hsa-Mir-96-P1_5p                    0.490109441851604    0.651795030916051
## Hsa-Mir-96-P2_5p                    0.943819521347161     0.98187676011116
## Hsa-Mir-96-P3_5p                    0.439662130177162    0.603366114817595
## 
## $tissue.type_tumor.colorect_vs_normal.colorect
## log2 fold change (MAP): tissue.type tumor.colorect vs normal.colorect 
## Wald test p-value: tissue.type tumor.colorect vs normal.colorect 
## DataFrame with 389 rows and 6 columns
##                                          baseMean      log2FoldChange
##                                         <numeric>           <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 89174.7542403281  -0.248058505837001
## Hsa-Let-7-P1b_5p                 3359.93816553835  -0.129712872859404
## Hsa-Let-7-P1c_5p                 3992.69006418194 -0.0894387113372064
## Hsa-Let-7-P2a3_5p                52000.6252125236   0.161274363696562
## Hsa-Let-7-P2b1_5p                9751.60360980849  -0.129885530327206
## ...                                           ...                 ...
## Hsa-Mir-95-P2_3p                 390.216333625945    1.16639047991208
## Hsa-Mir-95-P3_5p                 3.95328293251241   0.128041251542255
## Hsa-Mir-96-P1_5p                 202.656949570096    1.99605999628903
## Hsa-Mir-96-P2_5p                 27209.4573430933     1.8371528804308
## Hsa-Mir-96-P3_5p                 3344.18853507094     1.5305097819346
##                                               lfcSE               stat
##                                           <numeric>          <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p   0.09974589771869  -2.50880006172293
## Hsa-Let-7-P1b_5p                  0.160828091638605 -0.872616006654668
## Hsa-Let-7-P1c_5p                  0.198255228124062  -0.14744126618236
## Hsa-Let-7-P2a3_5p                0.0878077644317027   1.81863066456019
## Hsa-Let-7-P2b1_5p                0.0893412951279261  -1.43703312496472
## ...                                             ...                ...
## Hsa-Mir-95-P2_3p                  0.140554605668826   8.28848119000958
## Hsa-Mir-95-P3_5p                  0.264177931179274  0.737283525185868
## Hsa-Mir-96-P1_5p                  0.217577914511228   8.93516022919441
## Hsa-Mir-96-P2_5p                  0.173955103251303   10.4458859814953
## Hsa-Mir-96-P3_5p                  0.226074853962932   6.65702796429079
##                                                pvalue                 padj
##                                             <numeric>            <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p   0.0121142030924821   0.0250889779893481
## Hsa-Let-7-P1b_5p                     0.38287241295549    0.480492200364671
## Hsa-Let-7-P1c_5p                    0.882783735719796    0.926800653290313
## Hsa-Let-7-P2a3_5p                  0.0689677962587844    0.122143900850838
## Hsa-Let-7-P2b1_5p                   0.150708581526998    0.225175837320617
## ...                                               ...                  ...
## Hsa-Mir-95-P2_3p                  1.1470369823237e-16 1.40234521387317e-15
## Hsa-Mir-95-P3_5p                    0.460949948804283    0.561736432787213
## Hsa-Mir-96-P1_5p                 4.06588568948011e-19 8.56094820173867e-18
## Hsa-Mir-96-P2_5p                 1.53019828709604e-25 6.44383500899331e-24
## Hsa-Mir-96-P3_5p                 2.79420040427442e-11 1.96111472818519e-10
## 
## $tissue.type_normal.liver_vs_tumor.colorect
## log2 fold change (MAP): tissue.type normal.liver vs tumor.colorect 
## Wald test p-value: tissue.type normal.liver vs tumor.colorect 
## DataFrame with 389 rows and 6 columns
##                                          baseMean      log2FoldChange
##                                         <numeric>           <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 89174.7542403281   0.134516855928531
## Hsa-Let-7-P1b_5p                 3359.93816553835   -0.18431192252938
## Hsa-Let-7-P1c_5p                 3992.69006418194    3.24635633273589
## Hsa-Let-7-P2a3_5p                52000.6252125236 -0.0245108495144203
## Hsa-Let-7-P2b1_5p                9751.60360980849   0.451258084717704
## ...                                           ...                 ...
## Hsa-Mir-95-P2_3p                 390.216333625945   -1.41840794320829
## Hsa-Mir-95-P3_5p                 3.95328293251241   0.414413322947411
## Hsa-Mir-96-P1_5p                 202.656949570096   -4.81237877419974
## Hsa-Mir-96-P2_5p                 27209.4573430933   -4.46595236546167
## Hsa-Mir-96-P3_5p                 3344.18853507094   -5.07557947770261
##                                               lfcSE               stat
##                                           <numeric>          <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p   0.11131421654252   1.19461982343763
## Hsa-Let-7-P1b_5p                  0.184409102642062  -1.03024741017946
## Hsa-Let-7-P1c_5p                  0.232675048404373    13.987286383856
## Hsa-Let-7-P2a3_5p                0.0976442188709745 -0.267757085486139
## Hsa-Let-7-P2b1_5p                0.0994097267910815   4.53746602977892
## ...                                             ...                ...
## Hsa-Mir-95-P2_3p                  0.161409886777642    -8.797157721989
## Hsa-Mir-95-P3_5p                  0.332664401974054   1.34224159185026
## Hsa-Mir-96-P1_5p                  0.288868128705996  -16.6454924674176
## Hsa-Mir-96-P2_5p                  0.201189454623787  -22.1766896561084
## Hsa-Mir-96-P3_5p                  0.274372198803725  -18.4172382155403
##                                                 pvalue                  padj
##                                              <numeric>             <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p     0.232235600538294     0.301594555061476
## Hsa-Let-7-P1b_5p                     0.302893879225991     0.381823880327226
## Hsa-Let-7-P1c_5p                  1.86389083593418e-44  3.13619892828925e-43
## Hsa-Let-7-P2a3_5p                    0.788886305673403     0.867326705385247
## Hsa-Let-7-P2b1_5p                 5.69341978961745e-06   1.4887523368797e-05
## ...                                                ...                   ...
## Hsa-Mir-95-P2_3p                  1.40325015866435e-18  7.75796873433006e-18
## Hsa-Mir-95-P3_5p                     0.179517674775297     0.240392180408443
## Hsa-Mir-96-P1_5p                  3.26270971024334e-62  7.89167911165108e-61
## Hsa-Mir-96-P2_5p                 5.76691295122432e-109 4.46359062424763e-107
## Hsa-Mir-96-P3_5p                  9.55562651928938e-76  2.84463650997307e-74
## 
## $tissue.type_normal.lung_vs_tumor.colorect
## log2 fold change (MAP): tissue.type normal.lung vs tumor.colorect 
## Wald test p-value: tissue.type normal.lung vs tumor.colorect 
## DataFrame with 389 rows and 6 columns
##                                          baseMean     log2FoldChange
##                                         <numeric>          <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 89174.7542403281  0.408125846033093
## Hsa-Let-7-P1b_5p                 3359.93816553835  0.332599900506941
## Hsa-Let-7-P1c_5p                 3992.69006418194     2.020544361604
## Hsa-Let-7-P2a3_5p                52000.6252125236  0.129817388788356
## Hsa-Let-7-P2b1_5p                9751.60360980849   1.23298675588116
## ...                                           ...                ...
## Hsa-Mir-95-P2_3p                 390.216333625945 -0.987860472334413
## Hsa-Mir-95-P3_5p                 3.95328293251241 -0.191008452152824
## Hsa-Mir-96-P1_5p                 202.656949570096   -2.3341261742075
## Hsa-Mir-96-P2_5p                 27209.4573430933  -1.90878481829015
## Hsa-Mir-96-P3_5p                 3344.18853507094  -1.93676271625054
##                                              lfcSE               stat
##                                          <numeric>          <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.151440987364688   2.68337077689517
## Hsa-Let-7-P1b_5p                 0.249992618790543   1.30066611269162
## Hsa-Let-7-P1c_5p                 0.314717268191971   6.48763117983787
## Hsa-Let-7-P2a3_5p                0.132884439212234  0.963563940574538
## Hsa-Let-7-P2b1_5p                0.135208009802693   9.11432613077411
## ...                                            ...                ...
## Hsa-Mir-95-P2_3p                 0.215830901283507  -4.59605232940141
## Hsa-Mir-95-P3_5p                 0.415244927482672 -0.364000003055949
## Hsa-Mir-96-P1_5p                 0.350565916495824  -6.75367079090678
## Hsa-Mir-96-P2_5p                 0.272289955724401  -7.05756424076837
## Hsa-Mir-96-P3_5p                 0.366207615281681  -5.32493458906083
##                                                pvalue                 padj
##                                             <numeric>            <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p  0.00728841357441722   0.0138946603610811
## Hsa-Let-7-P1b_5p                    0.193372766419832    0.281397733924404
## Hsa-Let-7-P1c_5p                 8.71964116059556e-11 4.62260428650751e-10
## Hsa-Let-7-P2a3_5p                   0.335264592721268    0.444340401997023
## Hsa-Let-7-P2b1_5p                7.91608806736923e-20 9.88234220023191e-19
## ...                                               ...                  ...
## Hsa-Mir-95-P2_3p                  4.3057060677156e-06 1.35472215301296e-05
## Hsa-Mir-95-P3_5p                    0.715858007098838    0.805340257986192
## Hsa-Mir-96-P1_5p                 1.44150633672609e-11 8.45246897443934e-11
## Hsa-Mir-96-P2_5p                 1.69446438026879e-12 1.11145375451529e-11
## Hsa-Mir-96-P3_5p                 1.00989374164518e-07   3.947766444613e-07
## 
## $tissue.type_metastasis.liver_vs_tumor.colorect
## log2 fold change (MAP): tissue.type metastasis.liver vs tumor.colorect 
## Wald test p-value: tissue.type metastasis.liver vs tumor.colorect 
## DataFrame with 389 rows and 6 columns
##                                          baseMean      log2FoldChange
##                                         <numeric>           <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 89174.7542403281 -0.0291372578148132
## Hsa-Let-7-P1b_5p                 3359.93816553835 -0.0384102612949004
## Hsa-Let-7-P1c_5p                 3992.69006418194   0.165919575225585
## Hsa-Let-7-P2a3_5p                52000.6252125236 -0.0667790393731562
## Hsa-Let-7-P2b1_5p                9751.60360980849  -0.022055090655077
## ...                                           ...                 ...
## Hsa-Mir-95-P2_3p                 390.216333625945   0.197986570574988
## Hsa-Mir-95-P3_5p                 3.95328293251241    0.87517582162084
## Hsa-Mir-96-P1_5p                 202.656949570096  -0.153456519278348
## Hsa-Mir-96-P2_5p                 27209.4573430933  0.0668521603991884
## Hsa-Mir-96-P3_5p                 3344.18853507094   0.480378406060166
##                                               lfcSE               stat
##                                           <numeric>          <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p  0.087811822186451  -0.34524409879388
## Hsa-Let-7-P1b_5p                  0.143352612343951 -0.309464165623311
## Hsa-Let-7-P1c_5p                  0.178645206267475   1.08002602868534
## Hsa-Let-7-P2a3_5p                0.0771691545429361 -0.884124420143461
## Hsa-Let-7-P2b1_5p                0.0785334182092049 -0.274463101822189
## ...                                             ...                ...
## Hsa-Mir-95-P2_3p                  0.123880822993862   1.53530515265106
## Hsa-Mir-95-P3_5p                  0.226325334424462   3.88027612923505
## Hsa-Mir-96-P1_5p                  0.196070125088779 -0.994964941991442
## Hsa-Mir-96-P2_5p                  0.155589993744074  0.303386567615509
## Hsa-Mir-96-P3_5p                  0.205702483629426   2.17135483299889
##                                                pvalue                 padj
##                                             <numeric>            <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p    0.729910868456285    0.886990854282231
## Hsa-Let-7-P1b_5p                    0.756968466884636    0.898589554206931
## Hsa-Let-7-P1c_5p                    0.280130589584733    0.477579463300844
## Hsa-Let-7-P2a3_5p                   0.376629052036082    0.589523977412743
## Hsa-Let-7-P2b1_5p                   0.783728755707429     0.90746054828939
## ...                                               ...                  ...
## Hsa-Mir-95-P2_3p                    0.124708889065693    0.283896118049548
## Hsa-Mir-95-P3_5p                 0.000104337942960983 0.000776515075498082
## Hsa-Mir-96-P1_5p                     0.31975331550125    0.522757414533685
## Hsa-Mir-96-P2_5p                    0.761595281084944    0.898589554206931
## Hsa-Mir-96-P3_5p                   0.0299043603592752   0.0972519954541135
## 
## $tissue.type_metastasis.lung_vs_tumor.colorect
## log2 fold change (MAP): tissue.type metastasis.lung vs tumor.colorect 
## Wald test p-value: tissue.type metastasis.lung vs tumor.colorect 
## DataFrame with 389 rows and 6 columns
##                                          baseMean     log2FoldChange
##                                         <numeric>          <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 89174.7542403281 -0.371287975266059
## Hsa-Let-7-P1b_5p                 3359.93816553835  -0.86478570992646
## Hsa-Let-7-P1c_5p                 3992.69006418194  0.135121670461325
## Hsa-Let-7-P2a3_5p                52000.6252125236 -0.362077135000897
## Hsa-Let-7-P2b1_5p                9751.60360980849 0.0403928595342307
## ...                                           ...                ...
## Hsa-Mir-95-P2_3p                 390.216333625945  0.244457301347207
## Hsa-Mir-95-P3_5p                 3.95328293251241  0.770986123093289
## Hsa-Mir-96-P1_5p                 202.656949570096 -0.746540861417062
## Hsa-Mir-96-P2_5p                 27209.4573430933  -0.78147625945163
## Hsa-Mir-96-P3_5p                 3344.18853507094 -0.326709592212615
##                                               lfcSE              stat
##                                           <numeric>         <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.0959152266130106 -3.87096089411713
## Hsa-Let-7-P1b_5p                  0.156536850438983 -5.51447766923507
## Hsa-Let-7-P1c_5p                  0.194969507259272 0.835611129624883
## Hsa-Let-7-P2a3_5p                0.0842935080279992 -4.30344211748187
## Hsa-Let-7-P2b1_5p                0.0857694713451329 0.474626440937984
## ...                                             ...               ...
## Hsa-Mir-95-P2_3p                  0.135123404458061  1.75056733087186
## Hsa-Mir-95-P3_5p                   0.24193629758336  3.22815141458786
## Hsa-Mir-96-P1_5p                  0.213973544808363 -3.63856405996494
## Hsa-Mir-96-P2_5p                  0.169868922201866 -4.66162915975394
## Hsa-Mir-96-P3_5p                  0.224439059665663 -1.53101854305705
##                                                pvalue                 padj
##                                             <numeric>            <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p   0.0001084071837261  0.00040731631166991
## Hsa-Let-7-P1b_5p                 3.49817317913999e-08  2.5543264534475e-07
## Hsa-Let-7-P1c_5p                    0.403373705499327    0.532783699755084
## Hsa-Let-7-P2a3_5p                1.68164797190917e-05 7.65644429563353e-05
## Hsa-Let-7-P2b1_5p                   0.635053256632943    0.757086098142951
## ...                                               ...                  ...
## Hsa-Mir-95-P2_3p                   0.0800204667405637     0.14746628870761
## Hsa-Mir-95-P3_5p                  0.00124593006501447  0.00359832041164626
## Hsa-Mir-96-P1_5p                 0.000274162436246517  0.00098241539655002
## Hsa-Mir-96-P2_5p                 3.13716090303122e-06 1.57672892139361e-05
## Hsa-Mir-96-P3_5p                    0.125764809854213    0.211612962667741
## 
## $tissue.type_metastasis.pc_vs_tumor.colorect
## log2 fold change (MAP): tissue.type metastasis.pc vs tumor.colorect 
## Wald test p-value: tissue.type metastasis.pc vs tumor.colorect 
## DataFrame with 389 rows and 6 columns
##                                          baseMean      log2FoldChange
##                                         <numeric>           <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 89174.7542403281  -0.194926960102631
## Hsa-Let-7-P1b_5p                 3359.93816553835  -0.170006997664929
## Hsa-Let-7-P1c_5p                 3992.69006418194   0.950788049188073
## Hsa-Let-7-P2a3_5p                52000.6252125236  -0.235739655653004
## Hsa-Let-7-P2b1_5p                9751.60360980849 -0.0275610759199226
## ...                                           ...                 ...
## Hsa-Mir-95-P2_3p                 390.216333625945  -0.391736823339089
## Hsa-Mir-95-P3_5p                 3.95328293251241   0.193480385452117
## Hsa-Mir-96-P1_5p                 202.656949570096  -0.537251798926674
## Hsa-Mir-96-P2_5p                 27209.4573430933  -0.144468039225267
## Hsa-Mir-96-P3_5p                 3344.18853507094  -0.149480720489416
##                                               lfcSE               stat
##                                           <numeric>          <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.0919497346955033  -2.12106919715872
## Hsa-Let-7-P1b_5p                  0.146429745186075  -1.18446872666545
## Hsa-Let-7-P1c_5p                  0.178397588170818   5.34338828103208
## Hsa-Let-7-P2a3_5p                  0.08107727951498  -2.91425069775732
## Hsa-Let-7-P2b1_5p                0.0824764385946126  -0.32740564020296
## ...                                             ...                ...
## Hsa-Mir-95-P2_3p                  0.128019210385648  -3.07056591636714
## Hsa-Mir-95-P3_5p                  0.222349699850271  0.967689700538171
## Hsa-Mir-96-P1_5p                  0.193381569174697   -2.9161157752392
## Hsa-Mir-96-P2_5p                  0.157787812752101  -1.01238717153534
## Hsa-Mir-96-P3_5p                  0.201177138993338 -0.825123443737066
##                                                pvalue                 padj
##                                             <numeric>            <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p   0.0339159796632756   0.0901196031052751
## Hsa-Let-7-P1b_5p                    0.236227568744681    0.370368857833221
## Hsa-Let-7-P1c_5p                 9.12250626572605e-08 1.40060447984391e-06
## Hsa-Let-7-P2a3_5p                 0.00356543456685069   0.0141511415829511
## Hsa-Let-7-P2b1_5p                   0.743361101075885    0.848252544785979
## ...                                               ...                  ...
## Hsa-Mir-95-P2_3p                  0.00213653519975517    0.009575796316975
## Hsa-Mir-95-P3_5p                    0.333199363429196     0.48229635484693
## Hsa-Mir-96-P1_5p                  0.00354418957989829   0.0141511415829511
## Hsa-Mir-96-P2_5p                    0.311352969649893     0.46861091937278
## Hsa-Mir-96-P3_5p                    0.409301511166841    0.561003252050181
## 
## $type_metastasis_vs_tumor
## log2 fold change (MAP): type metastasis vs tumor 
## Wald test p-value: type metastasis vs tumor 
## DataFrame with 389 rows and 6 columns
##                                          baseMean       log2FoldChange
##                                         <numeric>            <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 89174.7542403281   -0.180701783114052
## Hsa-Let-7-P1b_5p                 3359.93816553835   -0.294623990307531
## Hsa-Let-7-P1c_5p                 3992.69006418194     0.48926666782258
## Hsa-Let-7-P2a3_5p                52000.6252125236   -0.206937259890988
## Hsa-Let-7-P2b1_5p                9751.60360980849 -0.00482780603867569
## ...                                           ...                  ...
## Hsa-Mir-95-P2_3p                 390.216333625945   0.0426916660211096
## Hsa-Mir-95-P3_5p                 3.95328293251241    0.678957110901101
## Hsa-Mir-96-P1_5p                 202.656949570096   -0.464482054965902
## Hsa-Mir-96-P2_5p                 27209.4573430933   -0.221824399033929
## Hsa-Mir-96-P3_5p                 3344.18853507094   0.0672687256455847
##                                               lfcSE               stat
##                                           <numeric>          <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.0639958634974693  -2.81887235438572
## Hsa-Let-7-P1b_5p                  0.106490720796663  -2.76160115635425
## Hsa-Let-7-P1c_5p                  0.149925843445819   3.33812788609782
## Hsa-Let-7-P2a3_5p                0.0563710904824311  -3.67052297626806
## Hsa-Let-7-P2b1_5p                0.0617282834747614 -0.070874324259544
## ...                                             ...                ...
## Hsa-Mir-95-P2_3p                 0.0918217873522948  0.437722915855047
## Hsa-Mir-95-P3_5p                  0.170411797990612   3.95560185906774
## Hsa-Mir-96-P1_5p                  0.152114644217898  -3.14528253438741
## Hsa-Mir-96-P2_5p                   0.12892939045268  -1.79236001772164
## Hsa-Mir-96-P3_5p                  0.164329004650402  0.314884459867911
##                                                pvalue                 padj
##                                             <numeric>            <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p  0.00481926786262815   0.0172690431744175
## Hsa-Let-7-P1b_5p                  0.00575186955495521   0.0200538154753844
## Hsa-Let-7-P1c_5p                  0.00084344919760993  0.00370925953948912
## Hsa-Let-7-P2a3_5p                0.000242054709304128  0.00121656068182724
## Hsa-Let-7-P2b1_5p                   0.943497778247006     0.97368970715091
## ...                                               ...                  ...
## Hsa-Mir-95-P2_3p                    0.661587155561431    0.823261187145574
## Hsa-Mir-95-P3_5p                 7.63422117073344e-05 0.000440961730309529
## Hsa-Mir-96-P1_5p                  0.00165926501866658  0.00690468346477385
## Hsa-Mir-96-P2_5p                   0.0730753150258787     0.17139482978797
## Hsa-Mir-96-P3_5p                    0.752849381089381     0.86454810231926